Cloud-based analytics for industrial automation

ABSTRACT

A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations. The analytics engine also can determine when there is a deviation or potential of deviation from desired system performance by an industrial asset or employee, and provide a notification, a recommendation, or an instruction to rectify or avoid the deviation.

TECHNICAL FIELD

The subject application relates generally to industrial automation, and,more particularly, to cloud-based analytics for industrial automation.

BACKGROUND

Industrial controllers and their associated input/output (I/O) devicescan be useful to the operation of modern industrial automation systems.These industrial controllers can interact with field devices on theplant floor to control automated processes relating to such objectivesas product manufacture, material handling, batch processing, supervisorycontrol, and other such applications. Industrial controllers can storeand execute user-defined control programs to effect decision-making inconnection with the controlled process. Such programs can include, butare not limited to, ladder logic, sequential function charts, functionblock diagrams, structured text, or other such programming structures.In general, industrial controllers can read input data from sensors andmetering devices that can provide discreet and telemetric data regardingone or more states of the controlled system, and can generate controloutputs based on these inputs in accordance with the user-definedprogram.

In addition to industrial controllers and their associated I/O devices,some industrial automation systems also can include low-level controlsystems, such as vision systems, barcode marking systems, variablefrequency drives, industrial robots, and the like, which can performlocal control of portions of the industrial process, or which can havetheir own localized control systems.

During operation of a given industrial automation system, comprising acollection of industrial devices, industrial processes, other industrialassets, and network-related assets, users (e.g., operators, technicians,maintenance personnel, etc.) typically can monitor or manage operationsof the industrial automation system, perform maintenance, repairs, orupgrades on the industrial automation system, or perform other tasks inconnection with operation of the industrial automation system. Theabove-described description of today's industrial control and businesssystems is merely intended to provide a contextual overview of relatingto conventional industrial automation systems, and is not intended to beexhaustive.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is intended to identify key/critical elements orto delineate the scope of the various aspects described herein. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

Presented herein are various systems, methods, and techniques of thedisclosed subject matter that relate to the use of data analysis (e.g.,big data analysis) in a cloud platform to facilitate performinganalytics on an industrial automation system(s) to improve performanceof the industrial automation system(s). An analytics component (e.g., acloud-based analytics engine) can be interfaced with an industrialautomation system(s) via a cloud gateway(s). A collection component(e.g., cloud-based collection component) can collect industrial-relateddata obtained from the industrial automation system(s) and/or other datasources (e.g., extrinsic data sources). The industrial-related data canbe stored in a data store (e.g., cloud-based data store) of orassociated with the analytics component.

The analytics component can analyze the data relating to an industrialautomation system(s) that can be obtained from the industrial automationsystem(s) or other sources (e.g., extrinsic data sources) to facilitateenhancing operation of the industrial automation system(s). Based atleast in part on the results of the data analysis, the analyticscomponent can determine one or more correlations between respectiveportions or aspects of (e.g., respective items of interest associatedwith) the industrial automation system(s), between a portion(s) oraspect(s) of the industrial automation system(s) and extrinsic events orconditions, or between an employee(s) and a portion(s) or aspect(s) ofthe industrial automation system(s). The analytics component candetermine recommendations or instructions in connection with theindustrial automation system(s) to enhance system performance based atleast in part on the determined correlation(s). The analytics componentcan provide (e.g., transmit) the recommendations or instructions to auser (e.g., employee) associated with the industrial automationsystem(s) or to the industrial automation system(s) to facilitateenhancing operational performance of the industrial automationsystem(s).

The analytics component also can determine when there is a deviation ora potential for deviation from desired (e.g., optimal, suitable,acceptable) performance (e.g., from a performance baseline) by anindustrial asset or an employee in connection with operation of theindustrial automation system(s), based at least in part on the resultsof the analysis of the industrial-related data. The analytics componentcan generate a notification, a recommendation, or an instruction tofacilitate rectifying or avoiding the deviation. The analytics componentcan communicate the notification, recommendation, or instruction to auser associated with the industrial automation system(s) or to theindustrial automation system(s) (e.g., to the industrial asset), whereinthe user or industrial automation system(s) can consider or take action(e.g., corrective or preventive action) in response to the notification,recommendation, or instruction to facilitate rectifying or avoiding thedeviation.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example system that can performanalytics on data associated with an industrial automation systemassociated with an industrial enterprise to facilitate determiningcorrelations between respective aspects of the industrial automationsystem to facilitate improving operations associated with the industrialautomation system, in accordance with various implementations andembodiments of the disclosed subject matter.

FIG. 2 depicts a block diagram of an example system that can performanalytics in connection with an industrial automation system(s) using amodel(s) of the industrial automation system(s) to facilitate improvingoperations of the industrial automation system(s), in accordance withvarious implementations and embodiments of the disclosed subject matter.

FIG. 3 illustrates a block diagram of an example system that can employsvisualization tools or techniques that can facilitate presentinganalytics-related information relating to an industrial automationsystem(s) to users, in accordance with various implementations andembodiments of the disclosed subject matter.

FIG. 4 presents a diagram of an example information display relating tothe industrial automation system, in accordance with variousimplementations and embodiments of the disclosed subject matter.

FIG. 5 presents a diagram of an example customized information displayrelating to two or more selected items of interest in relation to theindustrial automation system, in accordance with various implementationsand embodiments of the disclosed subject matter.

FIG. 6 presents a diagram of an example information display relating tothe industrial automation system, in accordance with variousimplementations and embodiments of the disclosed subject matter.

FIG. 7 presents a diagram of an example customized information displayrelating to selection of a portion of the information display inrelation to the industrial automation system, in accordance with variousimplementations and embodiments of the disclosed subject matter.

FIG. 8 illustrates a block diagram of an example system that can capturevideo of operations of an industrial automation system to facilitateperforming analytics in connection with the industrial automationsystem, in accordance with various implementations and embodiments ofthe disclosed subject matter.

FIG. 9 illustrates a block diagram of a high-level overview of anexample industrial enterprise that can leverage cloud-based services,including analytics services, modeling services, visualization services,data collection services, and data storage services, in accordance withvarious aspects and embodiments of the disclosed subject matter.

FIG. 10 presents a block diagram of an exemplary analytics componentaccording to various implementations and embodiments of the disclosedsubject matter.

FIG. 11 illustrates a diagram of an example system that can facilitateperforming analytics on data or generation of a model that can berepresentative of the industrial automation system, and the performanceof other analytics-related or model-related services based at least inpart collection of customer-specific industrial data by a cloud-basedanalytics system or modeling system, in accordance with various aspectsand embodiments of the disclosed subject matter.

FIG. 12 illustrates a diagram of an example hierarchical relationshipbetween these example data classes.

FIG. 13 depicts a block diagram of an example system that can beconfigured to comprise an industrial device that can act or operate as acloud proxy for other industrial devices of an industrial automationsystem to facilitate migrating industrial data to the cloud platform forclassification and analysis by an analytics system and a modeler system,in accordance with various aspects and implementations of the disclosedsubject matter.

FIG. 14 illustrates a block diagram of an example system that can employa firewall box that can serve as a cloud proxy for a set of industrialdevices to facilitate migrating industrial data to the cloud platformfor classification and analysis by an analytics system and a modelersystem, in accordance with various aspects and implementations of thedisclosed subject matter.

FIG. 15 illustrates a block diagram of an example device model accordingto various aspects and implementations of the disclosed subject matter.

FIG. 16 presents a block diagram of an example system that canfacilitate collection of data from devices and assets associated withrespective industrial automation systems for storage in cloud-based datastorage, in accordance with various aspects and implementations of thedisclosed subject matter.

FIG. 17 illustrates a block diagram of a cloud-based system that canemploy a analytics system and modeler system to facilitate performing orproviding analytics-related services and modeler-related servicesassociated with industrial automation systems, in accordance withvarious aspects and embodiments of the disclosed subject matter.

FIG. 18 illustrates a flow diagram of an example method that can performanalytics on industrial-automation-system-related data to determinecorrelations between respective items of interest associated with anindustrial automation system associated with an industrial enterprise,in accordance with various implementations and embodiments of thedisclosed subject matter.

FIG. 19 depicts a flow diagram of another example method that canperform analytics on industrial-automation-system-related data todetermine correlations between respective items of interest associatedwith a set of industrial automation systems associated with anindustrial enterprise and generate recommendations, notifications, orinstructions relating to the correlations to facilitate improvingoperation of the set of industrial automation systems, in accordancewith various implementations and embodiments of the disclosed subjectmatter.

FIG. 20 presents a flow diagram of an example method that can rank andprioritize respective correlations between respective items of interestassociated with an industrial automation system to facilitate improvingoperation of the industrial automation system, in accordance withvarious implementations and embodiments of the disclosed subject matter.

FIG. 21 illustrates a flow diagram of an example method that candetermine baselines for respective variables associated with anindustrial automation system and determine a deviation from a baselineof a variable associated with an industrial automation system, inaccordance with various implementations and embodiments of the disclosedsubject matter.

FIG. 22 depicts a flow diagram of an example method that can customizeinformation displays relating to correlations between respective itemsof interest that are associated with an industrial automation system, inaccordance with various implementations and embodiments of the disclosedsubject matter.

FIG. 23 is an example computing and/or operating environment.

FIG. 24 is an example computing and/or networking environment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding thereof. It may be evident, however, that the subjectdisclosure can be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate a description thereof.

Industrial automation systems can perform various processes to producedesired products or processed materials. An industrial automation systemcan comprise various industrial devices, industrial processes, otherindustrial assets, and network-related assets (e.g., communicationnetwork devices and software). During operation of a given industrialautomation system, users, such as, for example, operators, technicians,maintenance personnel, typically can monitor or manage operations of theindustrial automation system, perform maintenance, repairs, or upgradeson the industrial automation system, or perform other tasks inconnection with operation of the industrial automation system.

To that end, presented are various systems, methods, and techniques ofthe disclosed subject matter that relate to the use of data analysis(e.g., big data analysis) in a cloud platform to facilitate performinganalytics on an industrial automation system(s) to improve performanceof the industrial automation system(s). An analytics component (e.g., acloud-based analytics component) can be interfaced with an industrialautomation system(s) via a cloud gateway(s). A collection component(e.g., cloud-based collection component) can collect industrial-relateddata obtained from the industrial automation system(s) and/or other datasources (e.g., extrinsic data sources). The industrial-related data canbe stored in a data store (e.g., cloud-based data store) of orassociated with the analytics component.

The analytics component can analyze the industrial-related data obtainedfrom the industrial automation system(s) to facilitate enhancingoperation of the industrial automation system(s). Based at least in parton the results of the data analysis, the analytics component candetermine one or more correlations between respective portions oraspects of the industrial automation system(s), between a portion(s) oraspect(s) of the industrial automation system(s) and extrinsic events orconditions, or between an employee(s) and a portion(s) or aspect(s) ofthe industrial automation system(s). The analytics component candetermine recommendations or instructions in connection with theindustrial automation system(s) to enhance system performance based atleast in part on the determined correlation(s). The analytics componentcan provide (e.g., transmit) the recommendations or instructions to auser (e.g., employee) associated with the industrial automationsystem(s) or to the industrial automation system(s) to facilitateenhancing operational performance of the industrial automationsystem(s).

The analytics component also can determine when there is a deviation ora potential for deviation from desired (e.g., optimal, suitable,acceptable) performance (e.g., from a performance baseline) by anindustrial asset or an employee in connection with operation of theindustrial automation system(s), based at least in part on the resultsof the analysis of the industrial-related data. The analytics componentcan generate a notification, a recommendation, or an instruction tofacilitate rectifying or avoiding the deviation. The analytics componentcan communicate the notification, recommendation, or instruction to auser associated with the industrial automation system(s) or to theindustrial automation system(s) (e.g., to the industrial asset), whereinthe user or industrial automation system(s) can consider or take action(e.g., corrective or preventive action) in response to the notification,recommendation, or instruction to facilitate rectifying or avoiding thedeviation.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “controller,” “terminal,” “station,” “node,”“interface” are intended to refer to a computer-related entity or anentity related to, or that is part of, an operational apparatus with oneor more specific functionalities, wherein such entities can be eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical or magnetic storage medium)including affixed (e.g., screwed or bolted) or removably affixedsolid-state storage drives; an object; an executable; a thread ofexecution; a computer-executable program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers. Also,components as described herein can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry which is operated by asoftware or a firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that provides at least in part the functionality ofthe electronic components. As further yet another example, interface(s)can include input/output (I/O) components as well as associatedprocessor, application, or application programming interface (API)components. While the foregoing examples are directed to aspects of acomponent, the exemplified aspects or features also apply to a system,platform, interface, layer, controller, terminal, and the like.

As used herein, the terms “to infer” and “inference” refer generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of controllers includes one or more controllers; aset of data resources includes one or more data resources; etc.Likewise, the term “group” as utilized herein refers to a collection ofone or more entities; e.g., a group of nodes refers to one or morenodes.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

FIG. 1 illustrates a block diagram of an example system 100 that canperform analytics on data (e.g., cloud-based data) associated with anindustrial automation system associated with an industrial enterprise tofacilitate determining correlations between respective aspects of theindustrial automation system to facilitate improving operationsassociated with the industrial automation system, in accordance withvarious implementations and embodiments of the disclosed subject matter.The system 100 can comprise an analytics component 102 that can collecta vast array of data (e.g., industrial device related data, industrialprocess related data, other industrial asset related data, networkrelated data, other data) from industrial automation systems 104,process (and store) such data, perform analytics on such data (e.g., inthe cloud), and determine or identify correlations between respectiveaspects (e.g., between industrial assets, between parameters, or betweena portion of an industrial automation system 104 and an extrinsicevent(s) or condition(s)) associated with the industrial automationsystem(s) 104 to facilitate improving operations associated with theindustrial automation system(s) 104. The analytics component 102 canleverage such correlations to generate recommendations or instructionsregarding how to improve (e.g., enhance, optimize) operations of theindustrial automation system(s) 104 given current or anticipatedconditions relating to the industrial automation system(s) 104.

In some implementations, the analytics component 102 can reside in acloud platform, and can provide cloud-based services (e.g., analyticsservices, remote monitoring services, modeling services, data and/orcorrelation visualization services, virtualization services) inconnection with an industrial automation system(s) 104. The analyticscomponent 102 can use data analysis (e.g., big data analysis) in thecloud platform to facilitate performing analytics in connection with anindustrial automation system(s) 104 to improve performance of theindustrial automation system(s) 104. In other implementations, theanalytics component 102 can reside locally with an industrial automationsystem 104, wherein the analytics component can perform services tofacilitate improving performance of the industrial automation system104.

An analytics component 102 (e.g., a cloud-based analytics engine) can beinterfaced with the industrial automation system(s) 104 via a cloudgateway(s) (not shown in FIG. 1). For instance, an industrial device 110(e.g., an industrial controller) can comprise or be associated with acloud gateway component that can be employed to interface the analyticscomponent 102 with the industrial device 110. The analytics component102 can monitor (e.g., remotely monitor) operations of the industrialautomation system(s) 104. The analytics component can comprise or beassociated with a collection component 106 (e.g., cloud-based collectioncomponent) that can collect or obtain data (e.g.,industrial-automation-system-related data) from the industrialautomation system(s) 104 and/or other data sources (e.g., extrinsic datasources). The collection component 106 can store the collected data in adata store 108 (e.g., cloud-based data store) of or associated with theanalytics component 102 for future data analysis, and/or the analyticscomponent 102 can analyze the data as it is received by the analyticscomponent 102.

The industrial automation system 104 can comprise one or more industrialdevices 110, industrial processes 112, or other industrial assets 114that can be distributed throughout an industrial facility(ies) inaccordance with a desired industrial-automation-system configuration.The industrial automation system 104 can perform industrial processes orother actions to facilitate producing desired products, processedmaterials, etc., as an output.

The industrial automation system 104 also can include a networkcomponent 116 that can be associated with (e.g., integrated with,interfaced with, and/or communicatively connected to) the variousindustrial devices 110, industrial processes 112, and/or otherindustrial assets 114 of the industrial automation system 104 tofacilitate communication of information (e.g., command or controlinformation, status information, production information, etc.) betweenthe various industrial devices 110, industrial processes 112, and/orother industrial assets 114 via the network component 116. The networkcomponent 116, and/or all or a portion of the industrial device 110 orother industrial assets 114, can be associated with (e.g., interfacedwith, communicatively connected to (e.g., via a cloud gatewaycomponent(s)) the collection component 106 to facilitate thecommunication of data between the industrial automation system 104 andthe collection component 106. The network component 116 can comprisenetwork-related devices (e.g., communication devices, routers (e.g.,wireline or wireless routers), switches, etc.), wherein respectivenetwork-related devices can be connected to or interfaced with certainother network-related devices to form a communication network having adesired communication network configuration. In some implementations,one or more network-related devices of the network component 116 can beconnected to or interfaced with one or more industrial devices 110,industrial processes 112, and/or other industrial assets 114 tofacilitate collecting data (e.g., industrial-automation-system-relateddata) from the one or more industrial devices 110, industrial processes112, and/or other industrial assets 114 or communicating information(e.g., control signals, parameter data, configuration data, etc.) to theone or more industrial devices 110, industrial processes 112, and/orother industrial assets 114.

The analytics component 102 can monitor or track the operation of theindustrial automation system 104, including monitoring and tracking therespective operations of respective industrial devices 110, industrialprocesses 112, industrial assets 114, and/or network-related devices ofthe network component 116, and monitoring and tracking the configurationof the industrial automation system 104. The collection component 106can receive, obtain, detect, or collect data relating to the operationand configuration of the industrial automation system 104, as desired(e.g., automatically, dynamically, or continuously, in real or near realtime), in accordance with the defined analytics criteria. For example,the collection component 106 can receive data relating to the industrialdevices 110 (e.g., operation, status, or configurations of theindustrial devices, properties or characteristics of the industrialdevices, maintenance records of the industrial devices, employeesassociated with respective industrial devices), industrial processes 112(e.g., operation, status, or configurations of the industrial processes,properties or characteristics of the industrial processes, maintenancerecords associated with the industrial processes, employees associatedwith respective industrial processes), and the other industrial assets114 (e.g., operation, status, or configurations of the industrialassets, properties or characteristics of the industrial assets,maintenance records associated with the industrial assets, employeesassociated with respective industrial assets). The collection component106 also can receive or collect data relating to operation of thesub-components (e.g., network-related devices) of the network component116 (e.g., operation or status of the network devices or assets,communication conditions associated with a communication channel, totalbandwidth of a communication channel, available bandwidth of acommunication channel, properties or characteristics of the networkdevices or assets, maintenance records associated with the networkdevices or assets, configurations of the network devices or assets,employees associated with respective network-related devices).

The analytics component 102 can comprise an analytics managementcomponent 118 that can manage and implement the various services (e.g.,cloud-based services, such as analytics services, remote monitoringservices, visualization services, which, for example, can be availableas subscription services by an entity that owns, operates, or managesthe cloud platform and cloud-based services). The analytics managementcomponent 118 can analyze the industrial-automation-system-related dataobtained from the industrial automation system(s) 104 or other sourcesto facilitate enhancing operation of the industrial automation system(s)104.

Based at least in part on the results of the data analysis, theanalytics management component 118 can determine one or morecorrelations between respective portions or aspects of the industrialautomation system(s) (e.g., between a first industrial device 110 and asecond industrial device), between a portion(s) or aspect(s) of theindustrial automation system(s) and extrinsic events or conditions(e.g., between an industrial device 110 and a weather condition, orbetween an industrial process 112 and an inventory of a materialsupplier that provides a material(s) used in the industrial process112), or between an employee(s) and a portion(s) or aspect(s) of theindustrial automation system(s) (e.g., between an employee and anindustrial device(s) 110).

In some implementations, the analytics component 102 can normalize orstandardize the respective pieces of collected data to facilitate easierdetermination or identification of dependencies or correlations betweenrespective subsets of data. This can facilitate enabling an“apples-to-apples” comparison of data, as opposed to an“apples-to-oranges” comparison of data. For example, the analyticscomponent can normalize or standardize respective pieces of data basedat least in part on a unit of measurement of the data, a type of data, atype or characteristic of a data value represented by the data (e.g.,average data value, median data value, peak data value, standarddeviation associated with a data value, an amount of error associatedwith a data value), source of the data, and/or other factors, inaccordance with defined analytics criteria.

Based at least in part on one or more correlations determined by theanalytics management component 118, the analytics management component118 can determine one or more recommendations or instructions inconnection with the industrial automation system(s) 104 to enhanceperformance of the industrial automation system(s) 104, or anemployee(s) associated with the industrial automation system(s) 104. Theanalytics component can provide (e.g., transmit) the recommendations orinstructions to a communication device 120 associated with a user (e.g.,operator, technician, maintenance person, supervisor, informationtechnology (IT) personnel, or other personnel) associated with theindustrial automation system(s) 104 or to the industrial automationsystem(s) 104 (e.g., to an industrial device 110 or industrial process112) to facilitate enhancing operational performance of the industrialautomation system(s) 112 or an associated employee. The communicationdevice 120 can be, for example, (a handheld communication device), suchas a computer (e.g., a laptop computer), a mobile phone (e.g., a smartphone or other type of cellular phone), an electronic tablet, electroniceyeglasses (e.g., electronic eyeglasses (e.g., smart glasses) withcomputing and communication functionality), or other type ofcommunication device.

For example, based at least in part on a data analysis of collecteddata, the analytics management component 118 can determine a correlationbetween an industrial device 110 (e.g., industrial controller)associated with a first industrial process 112 and a second industrialprocess 112, wherein the operation of the second industrial process 112can be positively or negatively affected changes in the operation of theindustrial device 110. The analytics management component 118 candetermine that the current parameter settings of the industrial device110 are having a negative impact on (e.g., causing undesirable (e.g.,sub-optimal, unacceptable) performance by) the second industrial process112. The analytics management component 118 can determine modifiedparameter settings for the industrial device 110 that can improve theoperation of the second industrial process 112, while still performingin a desired (e.g., suitable, acceptable) manner with respect to thefirst industrial process 112.

The analytics component 102 can be interfaced with the industrial device110 via a cloud gateway component (e.g., integrated or otherwiseassociated with the industrial device 110). The analytics managementcomponent 118 can generate instructions that can facilitate changing thecurrent parameter settings to the modified parameter settings, and cancommunicate the instructions to the industrial device 110 via the cloudgateway component. The respective configurations and interfacing of theanalytics component 102 and industrial device 110 can thereby yield aclosed-loop control configuration that can facilitate enabling theanalytics component 102 to control (e.g., control configuration,parameter settings, operations of) the industrial device 110. Theindustrial device 110 can configure (e.g., re-configure) its parametersettings to change them from the current settings to the modifiedparameter settings, in response to the received instructions. Theindustrial device 110 can operate, based at least in part on themodified parameter settings, to facilitate improving the operation ofthe second industrial process 112. In other implementations (e.g.,alternatively), the analytics management component 118 can generate theinstructions or a corresponding recommendation to change the currentparameter settings of the industrial device 110 to the modifiedparameter settings, and/or other information relating to (e.g.,detailing) the correlation, and can communicate the instructions,recommendation, or other correlation-related information to a user(e.g., to a communication device 120 of the user) for considerationand/or action by the user to facilitate improving the operation of thesecond industrial process 112. In response to the instructions,recommendation, or other correlation-related information, the user cantake appropriate action (e.g., can change the current parameter settingsof the industrial device 110 to the modified parameter settings).

As another example, based at least in part on a data analysis ofcollected data, the analytics management component 118 can determine acorrelation between an external event, such as unusually high productinventory levels for a particular product in a chain of stores that wereidentified in recently obtained product inventory data, and an order forthe particular product that is scheduled to be serviced using a set ofindustrial devices 110 associated with an industrial process 112 of theindustrial automation system 104 used to produce the particular product.Further, based at least in part on the determined correlation, theanalytics management component 118 can determine that servicing theorder will result in unnecessarily producing more of the particularproduct and negatively impacting (e.g., reducing) the amount of revenuegenerated by the customer associated with the industrial facility. Inresponse to these determinations, the analytics management component 118can determine a different order for a different product that can andshould be serviced using the set of industrial devices 110 instead ofthe order, and can generate a notification, a recommendation, and/or aninstruction that can notify a user of the problems (e.g., undesirablyhigh product inventory levels for the particular product, reducedrevenue) associated with servicing the order, recommend running (e.g.,servicing) the different order using the set of industrial devices 110instead of the order, and/or instructing that the different order beserviced on the set of industrial devices 110 instead of the order. Theanalytics management component 118 can transmit the notification,recommendation, and/or instruction to the user (e.g., via thecommunication device 120) and/or the industrial automation system 104for consideration and/or action by the user and/or industrial automationsystem 104.

As still another example, based at least in part on a data analysis ofcollected data, the analytics management component 118 can determine acorrelation between a weather condition indicated in a weather forecastto occur at a particular geographical region at a particular time and asupply of an ingredient used in an industrial process 112 of theindustrial automation system 104. Further, based at least in part on thedetermined correlation, the analytics management component 118 candetermine that the weather condition will or at least is predicted tonegatively affect the supplying of the ingredient to the industrialfacility wherein the industrial automation system 104 resides, and thiswill or at least is predicted to have a negative affect on producing aparticular product that uses that ingredient via the industrial process112. In response to these determinations, the analytics managementcomponent 118 can determine that the industrial process should bemodified to a different industrial process that does not use or usesless of the ingredient to produce a different product than theindustrial process used to produce the particular product, to facilitatecompensating for the negative impact (e.g., process interruption ordowntime) or at least potential negative impact on the industrialprocess that may result from the negative affect or potential negativeaffect on the supply of the ingredient due to the forecasted weathercondition. The analytics management component 118 can generate anotification, a recommendation, and/or an instruction that can notify auser of the problem or potential problem with the supply of theingredient and its negative impact or potential negative impact on theproduction of the particular product, recommend modifying the industrialprocess to a different industrial process to produce a differentproduct, and/or instructing that the industrial process be modified to adifferent industrial process to produce a different product. Theanalytics management component 118 can transmit the notification,recommendation, and/or instruction to the user (e.g., via thecommunication device 120) and/or the industrial automation system 104for consideration and/or action by the user and/or industrial automationsystem 104.

To enhance the analysis (e.g., “golden batch” analysis to achieve the“golden batch” for production (e.g., optimal level of production) by theindustrial automation system 104), the analytics component 102 canincorporate other types of information, such as user (e.g., employee)behaviors with respect to the industrial automation system 104, theamount of time since an industrial device 110 has been maintained,serviced, repaired, or replaced, etc. For instance, the analyticscomponent 102 can monitor the work of employees in connection with anindustrial automation system 104 and can collect (e.g., via thecollection component 106) industrial-automation-system-related datarelating to the employees and the industrial automation system 104.Based at least in part on results from the analysis of such data, theanalytics management component 118 can identify one or more correlationsbetween one or more employees and the operation of the industrialautomation system 104 relating to the impact (e.g., negative impact,positive impact) the one or more employees have on the operation of theindustrial automation system 104. For example, based at least in part onresults from the analysis of such data, the analytics managementcomponent 118 can determine, identify, or learn that a certain employeeis more influential (e.g., in a positive manner) than another employeeon production outcomes for the industrial automation system 104. Forinstance, the analytics management component 118 can learn that when thecertain influential employee is absent from work, not only is there adirect negative impact on production in the industrial automation system104 due to that's employee's absence, but other employees do not work asproductively with respect to the industrial automation system 104 aswhen certain influential employee is at work (e.g., due to a negativechange in mood of the other employees due to the certain influentialemployee being absent, due to the lack of work guidance to those otheremployees from the certain influential employee, or for other reasons).The analytics management component 118 can generate a notification orrecommendation relating to the correlation between the employees and theoperation of the industrial automation system, and can communicate thenotification or recommendation to the user (e.g., to the communicationdevice 120 of the user) to facilitate improving operation of theemployees, improve operation of the industrial automation system 104,and/or at least notify the user regarding such correlation relating tothe employees in connection with production outcomes for the industrialautomation system 104.

In some implementations, the analytics component 102 can monitor andcollect data (e.g., via the collection component 106) regarding therespective focuses of attention of employees in connection with theirwork with the industrial automation system 104. Based at least in parton results from an analysis of such data, the analytics managementcomponent 118 can determine or identify correlations between therespective focuses of attention of employees (e.g., with regard to whereand when each employee's attention is focused) and production outcomesfor the industrial automation system 104. The analytics managementcomponent 118 also can determine one or more changes or recommendationsthat can be made to facilitate improving production outcomes for theindustrial automation system 104, wherein the changes or recommendationscan be, for example, changes in work schedules for the employees tofacilitate improving their attention and focus, changes in work tasks ofrespective employees, instructions regarding how employees can improvetheir attention or focus in connection with their work with theindustrial automation system 104, or other changes or recommendations.

For each industrial asset (e.g., 110, 112, 114) of the industrialautomation system 104, the analytics component 102 can monitor andcollect (e.g., via the collection component 106) information regardingthe amount of time that has elapsed since a particular industrial assethas been maintained, serviced, repaired, or replaced, as well as otherinformation (e.g., information, such as specifications, relating to theindustrial asset). Based at least in part on results from the analysisof such data, the analytics management component 118 can determine oridentify one or more correlations between the amount of time that haselapsed since a particular industrial asset has been maintained,serviced, repaired, or replaced and the operation of the industrialautomation system 104 relating to the impact (e.g., negative impact,positive impact) that the amount of time that has elapsed since themaintenance, servicing, repair, or replacement of the particularindustrial asset has on the operation of the industrial automationsystem 104.

For example, based at least in part on the analysis of information withrespect to an amount of time since a particular industrial device 110has been replaced, the analytics management component 118 can determinethat, with respect to the operation of the particular industrial device110, the production output for the industrial automation system 104 canbe expected to be desirable (e.g., optimal, suitable, acceptable) for aparticular length of time (e.g., 1 month, . . . , 6 months, . . . , 1year, . . . ), but after that particular length of time, the operationof the particular industrial device 110 can become less desirable (e.g.,less than optimal, suitable, acceptable), which can correlate to a lessthan desirable (e.g., less than optimal, suitable, acceptable)production output for the industrial automation system 104. In responseto this analysis, the analytics management component 118 can determine,and can generate a recommendation, that the industrial device 110 is tobe replaced when the particular length of time of operation of theindustrial device 110 has elapsed. The recommendation can be provided toa user (e.g., a maintenance technician, a service technician, or otherplant personnel) for appropriate action (e.g., replacement of theindustrial device 110) to be taken or considered.

When an industrial enterprise comprises or is associated with multipleindustrial automation systems 104 (e.g., multiple facilities or plants),the analytics component 102 can monitor and collect (e.g., via thecollection component 106) respectiveindustrial-automation-system-related data from the respective industrialautomation systems 104, including the respective industrial devices 110,industrial processes 112, other industrial assets 114, andnetwork-related devices of the network components 116 of the respectiveindustrial automation systems 104 in connection with the respectiveoperations of the respective industrial automation systems 104. Theanalytics management component 118 can aggregate the respectiveindustrial-automation-system-related data collected from the respectiveindustrial automation systems 104. The analytics management component118 also can analyze the respective industrial-automation-system-relateddata, and can determine correlations between industrial automationsystems 104, correlations between an industrial device and anotherindustrial device (e.g., that can span across multiple industrialautomation systems 104), correlations between an operation and anotheroperation (e.g., that can span across multiple industrial automationsystems 104), and/or other correlations based at least in part on theanalysis results. The analytics management component 118 can generateone or more instructions, recommendations, or notifications that canprovide information to facilitate modification of one or more of theindustrial automation systems 104 and/or notify a user regardingsystem-related correlations or recommended modifications to facilitateimproving the overall operations of the multiple industrial automationsystems 104 for the industrial enterprise. The analytics managementcomponent 118 can communicate the one or more instructions,recommendations, or notifications to the appropriate industrialautomation system(s) 104 or the appropriate user(s) for action orconsideration by the appropriate industrial automation system(s) 104 orappropriate user(s).

For example, based at least in part on a data analysis across multipleindustrial automation systems 104, the analytics management component118 can determine a correlation between transportation costs totransport products produced by the industrial enterprise at one or moreof the industrial automation systems 104 and the location of demand forthe products by consumers. Also, the analytics management component 118can determine that shifting a portion of the production load relating toproduction of the product from a first industrial automation system 104to a second industrial automation system 104 that is closer to thelocation of the consumer demand for the product can be accomplished withrespect to the first industrial automation system 104 and secondindustrial automation system 104, and can result in a reduction intransportation costs associated with transporting the product to productdistributors or retail stores that service the area of the consumerdemand and an increase in overall profit for the industrial enterprise,based at least in part on the analysis results. The analytics managementcomponent 118 can generate one or more instructions, recommendations, ornotifications relating to the determination that shifting a portion ofthe production load from the first industrial automation system 104 tothe second industrial automation system 104 can result in transportationcost reduction and increased profits for the industrial enterprise. Theanalytics management component 118 can communicate the one or moreinstructions, recommendations, or notifications to the first industrialautomation system 104, the second industrial automation system 104,and/or associated users (e.g., communication devices 120 of theassociated users), for action or consideration by the first industrialautomation system 104, the second industrial automation system 104,and/or associated users.

In some implementations, the analytics component 102 can monitor andtrack operations of the industrial automation system(s) 104 over time,and collect and store data (e.g., via the collection component 106 anddata store 108, respectively) relating to such monitoring and tracking.The analytics management component 118 can analyze the data to generatepertinent analysis results that can facilitate improving operation ofthe industrial automation system 104. Based at least in part on theresults of the data analysis, the analytics management component 118 candetermine respective baselines (e.g., performance baselines, guidelines)or formulas for respective variables associated with the industrialautomation system 104 that can indicate suitable (e.g., optimal,acceptable, preferred) operation of the industrial automation system104, or portion thereof, wherein the respective variables can bevariables that can affect output of the industrial automation system104, efficiency or power consumption of the industrial automation system104, material usage of an industrial device or process of the industrialautomation system 104, employee performance or behavior with respect tothe industrial automation system 104, etc. The respective formulas ofthe respective variables can indicate the respective effects therespective variables can have on the output of the industrial automationsystem 104, efficiency or power consumption of the industrial automationsystem 104, material usage of an industrial device or process of theindustrial automation system 104, employee performance or behavior withrespect to the industrial automation system 104, etc. The definedanalytics criteria can indicate or specify a defined threshold level ofsuitability or acceptability that can be applied to a baseline (e.g.,performance baseline) by the analytics management component 118. Theanalytics management component 118 can set the respective baselines forthe respective variables, wherein the respective baselines can be usedas respective guidelines for satisfying (e.g., achieving) a desiredproduction goal(s) (e.g., desirably high or efficient production output)for the industrial automation system 104, in accordance with definedoperation criteria. The variables can be or can relate to, for example,a configuration of an industrial asset (e.g., 110, 112, 114) ornetwork-related device, operational variable (e.g., speed of aconveyor), a production output(s), material inventory level, rawmaterial cost, energy cost, employee behavior (e.g., amount or qualityof work performed, attentiveness or focus on work tasks), or othervariables. Based at least in part on the results of the data analysis,the analytics management component 118 also can determine respectiveimpacts (e.g., negative impacts, positive impacts) of the respectivevariables on the operation of the industrial automation system 104, andcan use these determined respective impacts of the respective variablesto facilitate maintaining the respective baselines and satisfying thedesired production goal(s).

With the respective baselines for the respective variables associatedwith the industrial automation system 104 set, the analytics managementcomponent 118 can monitor and track operation of the industrialautomation system 104 and respective employees' behaviors in connectionwith the operation of the industrial automation system 104. Theanalytics management component 118 can identify and track respectiveemployees based at least in part on recognition techniques (e.g., facialrecognition, fingerprint recognition, iris recognition), identifiersassociated with their respective communication devices (e.g., 120), orrespective radio-frequency identification (RFID) tags, for example. Theanalytics component 102 can collect and store data (e.g., via thecollection component 106 and data store 108, respectively) relating tothis monitoring and tracking. The analytics management component 118 cananalyze this data to generate pertinent analysis results that canfacilitate improving or maintaining desired operation of the industrialautomation system 104, for example, by satisfying the desired productiongoal(s) associated with the industrial automation system 104. Based atleast in part on the results of the data analysis and the respectivebaselines for the respective variables, the analytics managementcomponent 118 can determine when there is a deviation or a potential fordeviation from suitable (e.g., optimal, acceptable, preferred)performance by an industrial asset (e.g., 110, 112, 114) or an employeein connection with operation of the industrial automation system 104.For instance, the analytics management component 118 can determine whenthere is a deviation or a potential for deviation (e.g., a trend towardsdeviation) from a baseline for a variable in connection with operationof the industrial automation system 104, wherein the deviation orpotential for deviation can indicate that the variable is unsuitable(e.g., not optimal, not acceptable, not preferred) or at least has thepotential to be or become unsuitable.

In response to determining or detecting a deviation or potential fordeviation from a set baseline for a variable associated with operationof the industrial automation system 104, the analytics managementcomponent 118 can generate a notification, a recommendation, or aninstruction that can comprise information and/or commands that canfacilitate rectifying or avoiding the deviation from the set baselinefor the variable. The analytics management component 118 can communicatethe notification, recommendation, or instruction to a user (e.g., viathe communication device 120) associated with the industrial automationsystem 104 or to the industrial automation system 104 (e.g., to anindustrial device 110), wherein the user or industrial automation system104 can consider or take action (e.g., corrective or preventive action)in response to the notification, recommendation, or instruction tofacilitate rectifying or avoiding the deviation from the set baselinefor the variable. The information relating to the deviation or potentialfor deviation can comprise, for example, a recommendation or instruction(e.g., command) to alter operation of one or more industrial assets(e.g., 110, 112, 114) to compensate for or rectify the deviation orpotential for deviation from the set baseline(s) for the variable(s)based at least in part on the determined or estimated (e.g., by theanalytics management component 118) effect of altering operation of theone or more industrial assets with respect to the deviation or potentialfor deviation, or a recommendation to have one or more employees changehow they perform certain work tasks in connection with operation of theindustrial automation system 104 based at least in part on thedetermined or estimated (e.g., by the analytics management component118) effect of changing how the one or more employees perform thecertain work tasks with respect to the deviation or potential fordeviation.

The analytics component 102 can continue to monitor and track operationsof the industrial automation system(s) 104 over time, and collect andstore data (e.g., via the collection component 106 and data store 108,respectively) relating to such monitoring and tracking. The analyticsmanagement component 118 can analyze additional data that is collectedto generate pertinent analysis results that can facilitate maintainingdesirable operation and/or improving operation of the industrialautomation system 104.

Based at least in part on the results of the data analysis, theanalytics management component 118 can determine whether respectivebaselines (e.g., performance baselines, guidelines) or formulas for therespective variables associated with the industrial automation system104 are to be modified to facilitate maintaining desirable operationand/or improving operation of the industrial automation system 104. Ifthe analytics management component 118 determines that a formulaassociated with a variable has changed over time, wherein, for example,the analytics management component 118 determines that the variable nolonger has the same influence on the output (e.g., production output) ofthe industrial automation system 104, efficiency or power consumption ofthe industrial automation system 104, material usage of an industrialdevice or process of the industrial automation system 104, employeeperformance or behavior with respect to the industrial automation system104, and/or other aspects associated with the industrial automationsystem 104, the analytics management component 118 can determine thatthe baseline for the variable is to be modified to a new value or level.

For example, if the analytics management component 118 determines that aformula associated with a particular variable has changed over time,with the particular variable becoming more of an influence on an output(e.g., a desired or critical production output) of the industrialautomation system 104, the analytics management component 118 can adjustthe formula and the baseline for the particular variable to reflect orcorrespond to the increased influence that the particular variable hason the output of the industrial automation system 104 (e.g., relative toother variables that can have an influence on the output of theindustrial automation system 104. Conversely, if the analyticsmanagement component 118 determines that a formula associated with aparticular variable has changed over time, with the particular variablebecoming less of an influence on an output of the industrial automationsystem 104, the analytics management component 118 can adjust theformula and the baseline for the particular variable to reflect orcorrespond to the decreased influence that the particular variable hason the output of the industrial automation system 104 (e.g., relative toother variables that can have an influence on the output of theindustrial automation system 104).

The analytics management component 118 also can perform data analysis(e.g., big data analysis) on aggregated customer data relating torespective industrial automation systems (e.g., 104) of respectivecustomers to facilitate anonymous knowledge sharing among the customers,wherein the collection component 106 can collect respective customerdata from the respective industrial automation systems (e.g., 104) ofthe respective customers. The analytics management component 118 canhave information that can enable the analytics management component 118to be aware of the respective industry(ies) of respective customers. Theanalytics management component 118 can aggregate respective subsets ofcustomers, and respective portions of data associated with thosecustomers, based at least in part on the respective industry(ies) of therespective customers, to facilitate collective analysis of thoseportions of data associated with a same or similar industry (e.g., tofacilitate identifying industry-specific trends or industry-specificcorrelations). Based at least in part on the data analysis on theaggregated customer data, the analytics management component 118 candetermine or learn that a particular system configuration for performinga particular industrial process (e.g., 112) in a given industry (e.g.,beverage industry) can result in a relatively higher output or lessdowntime than other system configurations. In response to thisdetermination, the analytics management component 118 can generate arecommendation to adopt the particular system configuration forperforming the particular industrial process, and can communicate therecommendation to one or more customers (e.g., to one or morecommunication devices of the one or more communication devices) in thegiven industry that may find such recommendation beneficial to them(e.g., can improve their respective industrial automation systems). Theinformation in the recommendation (e.g., recommendation message) can betailored such that it does not include any data of a particular customerthat can enable another customer to identify the data as being relatedto or obtained from the particular customer to facilitate maintainingthe anonymity of customers with respect to particular pieces of datathat have been collected by the collection component 106.

In some implementations, the analytics component 102 can generate andmaintain an asset strategy library that can be employed to facilitatemanaging the use, maintenance or servicing, replacement, and/or otheraspects of a set of industrial assets (e.g., 110, 112, 114) associatedwith an industrial automation system(s) 104. Based at least in part onthe results of the analysis of the data, the analytics managementcomponent 118 can determine use, maintenance, and replacement strategiesand schedules for use in managing the industrial assets. In someimplementations, the analytics management component 118 can employpredictive analysis to facilitate predicting or determining the use,maintenance, and replacement strategies and schedules to be used inconnection with the industrial assets. The analytics managementcomponent 118 can generate notifications, recommendations, and/orinstructions relating to the determined use, maintenance, andreplacement strategies and schedules associated with the industrialassets. The analytics management component 118 can communicate suchnotifications, recommendations, and/or instructions to a user (e.g., viaa communication device 120 of the user) and/or the industrial automationsystem(s) 104 for consideration and/or action by the user and/orindustrial automation system(s) 104.

In certain implementations, the analytics component 102 can enableanalysis functions relating to use, maintenance or servicing,replacement, and/or other aspects of an industrial asset (e.g., 110,112, or 114) to be distributed among the analytics component 102 and theindustrial asset. For example, from the cloud platform, the analyticsmanagement component 118 can provide (e.g., communicate) information(e.g., information relating to the specification of an industrial device110, maintenance of the industrial device 110, replacement of theindustrial device 110 (e.g., lifespan of the industrial device 110),etc.), instructions (e.g., analysis instructions), and/or an algorithm(e.g., analysis algorithm) to the industrial device 110. Based at leastin part on the information, instructions, and/or algorithm provided tothe industrial device 110, the industrial device 110 can performanalysis functions locally on itself (e.g., can self-analyze itself) to,for example, determine when the industrial device 110 should havemaintenance or servicing performed on it or when the industrial device110 should be replaced with a new industrial device 110. If, based atleast in part on its self-analysis, the industrial device 110 determinesthat it should have maintenance or servicing performed on it ordetermines the industrial device 110 should be replaced with a newindustrial device 110, the industrial device 110 can communicate anotification to the analytics component 102 or to a user (e.g., amaintenance or service technician or other plant personnel) to informthe analytics component 102 or the user that the industrial device 110should have maintenance or servicing performed on it or the industrialdevice 110 should be replaced with a new industrial device 110, whereinthe analytics component 102 or user can take or consider takingappropriate action (e.g., action to have the industrial device 110maintained, serviced, or replaced), as desired.

As disclosed herein, the analytics component 102 can reside on the cloudplatform, can collect data (e.g., via the collection component 106) fromthe industrial automation system 104 of the industrial facility (e.g.,plant floor at the industrial facility) via cloud gateways, performanalytics on the data and determine correlations relating to respectiveaspects associated with the industrial automation system 104, and cancommunicate notifications, recommendations, or instructions (e.g.,control instructions) to the industrial automation system 104 or usersbased at least in part on the results of the analytics. In otherimplementations, the analytics component 102 can reside locally (e.g.,on a private cloud platform or server) with respective to the industrialautomation system 104, for example, if a customer does not desire tomigrate certain data (e.g., sensitive data associated with the customer)to the cloud platform.

Referring to FIG. 2, depicted is a block diagram of an example system200 that can perform analytics in connection with an industrialautomation system(s) using a model(s) of the industrial automationsystem(s) to facilitate improving operations of the industrialautomation system(s), in accordance with various implementations andembodiments of the disclosed subject matter. The system 200 can comprisean analytics component 202, an industrial automation system(s) 204, acollection component 206, and a data store 208, wherein the industrialautomation system(s) 204 can comprise industrial devices 210, industrialprocesses 212, other industrial assets 214, and a network component 216,and wherein the analytics component 202 can comprise an analyticsmanagement component 218. The system 200 also can comprise or beassociated with a communication device 220, which can be associated withthe analytics component 202, industrial automation system(s) 204,collection component 206, and/or data store 208.

The system 200 also can comprise a modeler component 222 (e.g., acloud-based modeler component) that can employ and provide a variety ofservices including a cloud-based model generation service. The modelercomponent 222 can facilitate generation and management of a model 224that can correspond to the industrial automation system 204 based atleast in part on data (e.g., industrial-automation-system-related data)obtained from the industrial automation system 204, another industrialautomation system(s), or from other sources (e.g., extrinsic sources),in accordance with defined modeling criteria. As more fully disclosedherein, the collection component 206 can collectindustrial-automation-system-related data from one or more industrialautomation systems 204 of one or more industrial customers (e.g.,industrial enterprises) for storage (e.g., in the data store 208) andanalysis (e.g., by the modeler component 222 and/or analytics component202) on a cloud platform. As part of providing the cloud-based modelgeneration service, the modeler component 222 (and/or the analyticscomponent 202) can perform data analysis (e.g., big data analysis) onthe data in a cloud platform to facilitate generating the model 224 ofthe industrial automation system 204 that can be used to facilitateinteracting with (e.g., remotely monitoring operation of, trackingoperation of, controlling operation of, troubleshooting problems with,providing assistance relating to, etc., via a communication device) theindustrial automation system 204.

The modeler component 222 (and/or the analytics component 202) canmonitor or track the operation of the industrial automation system 204,including monitoring and tracking the respective operations ofrespective industrial devices 210, industrial processes 212, industrialassets 214, and/or network-related devices of the network component 216,and monitoring and tracking the configuration of the industrialautomation system 204. The modeler component 222 can generate aninteractive model(s) 104 of one or more industrial automation systems204 (e.g., of an industrial plant environment(s)), based at least inpart on the data analysis performed on the data (e.g.,industrial-automation-system-related data) relating to the operation ofthe industrial automation system 204 by the modeler component 222 and/oranalytics component 202.

In some implementations, the modeler component 222 can facilitateproviding cloud-based services (e.g., modeling services, troubleshootingservices, optimization services, remote viewing or controlling services,and/or other cloud-based services) to users and an industrial automationsystem(s) 204. Users (e.g., operators, technicians, maintenancepersonnel, supervisors, IT personnel, or other plant personnel) caninteract with a model 224 (e.g., interactive model), or a virtualizedindustrial automation system generated based on the model 224, of anindustrial automation system(s) 204 to perform various work tasks,functions, and/or operations, etc. For instance, a user can interactwith the model 224 or the corresponding virtualized industrialautomation system to facilitate remote viewing of, interaction with,troubleshooting of problems with, controlling operation of, and/oroptimization of industrial assets (e.g., industrial devices 210,industrial processes 212, other assets 214) or the network-relateddevices of the network component 216 of the industrial automationsystem(s) 204.

The industrial assets (e.g., industrial devices 210, industrialprocesses 212, other assets 214) and network-related components of thenetwork component 216 of an industrial automation system(s) 204 can beequipped with or associated with components, tools, functions, etc.,that can allow the modeler component 222 or analytics component 202 toinventory such industrial assets (e.g., 210, 212, 214) andnetwork-related components of the network component 216 from the cloud,wherein the modeler component 222 can generate a model 224 of theindustrial automation system(s) 202 based at least in part on the dataobtained from such inventory. The modeler component 222 or analyticscomponent 202 can poll (e.g., request information from) industrialassets, such as industrial devices 210, industrial processes 212, orother industrial assets 214, and/or network-related components of thenetwork component 216 via cloud gateway components (not shown in FIG. 2)to facilitate obtaining information regarding the industrial assets(e.g., 210, 212, 214) or network-related components of the networkcomponent 216 from the industrial assets (e.g., 210, 212, 214) ornetwork-related components. For example, an industrial asset (e.g., 210,212, 214) and/or a network-related component of the network component216 can comprise (e.g., be integrated with) or be associated with acloud gateway component that can enable the industrial asset (e.g., 210,212, 214) and/or network-related component to communicate with themodeler component 222 or analytics component 202 in the cloud tofacilitate the modeler component 222 or analytics component 202discovering, obtaining information from, analyzing information relatingto, and/or modeling the industrial asset (e.g., 210, 212, 214) and/ornetwork-related component of the network component 216. The informationcan comprise, for example, identification information (e.g.,identifiers) that can identify an industrial asset (e.g., 210, 212, 214)or network-related component, configuration information that canidentify a configuration of an industrial asset (e.g., 210, 212, 214) ornetwork-related component, contextual information relating to anindustrial asset (e.g., 210, 212, 214) or network-related device of thenetwork component 216, information relating functional or geographicalrelationships between industrial assets (e.g., 210, 212, 214) or betweenan industrial asset (e.g., 210, 212, 214) and a network-related deviceof the network component 216, information relating to a layout (e.g.,functional layout, logic layout, geographical layout) of an industrialautomation system 204, communication network connections, or otherinformation.

In some implementations, an industrial automation system 204 can containlegacy industrial assets (e.g., legacy industrial devices or otherlegacy industrial assets) or legacy network-related components that donot comprise or are not directly associated with a cloud gatewaycomponent. The communication device 220 (e.g., computer, mobile phone,electronic tablet or pad, electronic glasses) can be employed tofacilitate inventorying and collecting information relating to suchlegacy industrial assets or legacy network-related components. Forinstance, the communication device 220 can comprise a camera that can beused to take one or more pictures of legacy industrial assets, legacynetwork-related components, other industrial assets or network-relatedcomponents in proximity to the legacy industrial assets or legacynetwork-related components, and/or an area of the plant in proximity toa legacy industrial asset or legacy network-related component. Forinstance, the communication device 220 can take a picture of nameplateor other identifier information on a legacy industrial asset or legacynetwork-related component to facilitate identifying the legacyindustrial asset or legacy network-related component. The communicationdevice 220 can comprise a recognizer component (not shown in FIG. 2)that can recognize (e.g. using pattern or optical character recognition(OCR) recognition) or identify the legacy industrial asset or legacynetwork-related component based at least in part on information obtainedvia the photograph. Information relating to legacy industrial assets orlegacy network-related components also can be input to the communicationdevice 220 by a user via a keyboard, keypad, or audio interface (e.g., amicrophone that receives information from the user via the user'svoice).

The communication device 220 can interface with the cloud (e.g., via awireline or wireless communication connection), including with theanalytics component 202 and/or modeler component 222, to communicate(e.g., migrate) the information relating to legacy industrial assets orlegacy network-related components to the analytics component 202 and/ormodeler component 222 (e.g., via the collection component 206). Thecollection component 206 can facilitate storing this information in thedata store 208.

The modeler component 222 can model the industrial automation system204, including modeling industrial assets (e.g., 210, 212, 214), legacyindustrial assets, network-related devices (e.g., of the networkcomponent 216), and/or legacy network-related devices, based at least inpart on the respective information obtained from the industrial assets(e.g., 210, 212, 214), network component 216, and/or communicationdevice 220, to generate the interactive model 224 (e.g., a data-richinteractive model) of the industrial automation system 204. Tofacilitate generating a model 224 that can correspond to and beassociated with (e.g., can interact or be interfaced with) theindustrial automation system 204, the modeler component 222 can accessthe data store 208 (e.g., cloud-based data store) to obtain a set ofdata relating to the industrial automation system 204 and/or anotherindustrial automation system (e.g., another system comprising anindustrial device(s), process(es), and/or asset(s) that can be the sameor similar to an industrial device(s) 210, process(es) 212, and/orasset(s) 214 of the industrial automation system 204). The set of datacan comprise information relating to, for example, analytics datagenerated by the analytics component 202 based at least in part on ananalysis of data obtained from the industrial automation system 204 orfrom another data source; a pre-deployed model of an industrial asset(e.g., 210, 212, 214) or a network-related device that can be stored onthe industrial asset or network-related device and provided to theanalytics component 202 or modeler component 222 by the industrial assetor network-related device (or by an extrinsic data source); therespective properties, characteristics, functions, configurations, etc.,of respective industrial devices 210, industrial processes 212, otherindustrial assets 214, or network-related devices of the networkcomponent 216; or the configuration of industrial devices 210,industrial processes 212, and/or other industrial assets 214 in relationto each other. For example, the properties or characteristics forindustrial devices 210 or industrial processes 212 can comprisemechanical or process properties or characteristics associated withindustrial devices or processes (e.g., mechanical latency, process cycletimes, operating schedules, etc., associated with industrial devices).As another example, the properties or characteristics fornetwork-related devices can comprise communication properties orcharacteristics (e.g., wireless and/or wireline communicationfunctionality, type(s) of network or communication protocol(s), networkor communication specifications, total bandwidth, etc.) of therespective network-related devices.

The set of data also can comprise information relating to, for example,the configuration of the network-related devices in relation to eachother, or the configuration of network-related devices in relation tothe industrial devices 210, industrial processes 212, and/or otherindustrial assets 214; software, firmware, and/or operating systemutilized by the industrial automation system 204 (e.g., type(s),version(s), revision(s), configuration(s), etc., of the software,firmware, and/or operating system); functional and communicativerelationships between industrial devices 210, industrial processes 212,industrial assets 214, network-related devices of the network component216, etc. (e.g., communication connections or conditions betweenindustrial devices, types of connections between industrial devices,communication connections between industrial devices and network-relateddevices, etc.). The set of data further can include information relatingto, for example, human behavior or interaction in connection with theindustrial automation system 204 (e.g., maintenance schedules,shift-specific or operator-specific behavior or interaction of operatorswith the industrial automation system); production or process flows ofthe industrial automation system 204 at particular times or inconnection with particular projects; and/or other aspects or features ofthe industrial automation system 204.

The modeler component 222 can analyze the set of data and can generatethe model 224 of the industrial automation system 204 based at least inpart on the results of the analysis of the set of data. In someimplementations, the modeler component 222 can generate the model 224,which can be a multidimensional (e.g., two-dimensional (2-D) orthree-dimensional (3-D)) model, in accordance with an InternationalStandardization Organization (ISO) standard(s).

The modeler component 222 also can facilitate generation of amulti-dimensional (e.g., 2-D or 3-D) visualization or virtualization ofthe industrial automation system 204. The multi-dimensionalvirtualization of the industrial automation system 204 can be used(e.g., interacted with by a user) to facilitate remote viewing of,interaction with, troubleshooting of problems with, controllingoperation of, determining and/or generating optimization recommendationsfor, and/or optimization of industrial assets (e.g., 210, 212, 214) orthe network component 216 of the industrial automation system 204.

When there are multiple industrial plant facilities, the modelercomponent 222 can generate a model 224 that can represent (e.g., model)the multiple industrial automation systems (e.g., 204) of the multipleindustrial plant facilities and/or respective models (e.g., sub-models)of the respective industrial automation systems (e.g., 204) of therespective industrial plant facilities. The modeler component 222 alsocan facilitate generation of a multi-dimensional visualization orvirtualization of the multiple industrial automation systems (e.g., 204)that can be interacted with by users to facilitate remote viewing of,interaction with, troubleshooting of problems with, controllingoperation of, determining and/or generating optimization recommendationsfor, and/or optimization of industrial assets (e.g., 210, 212, 214) ofthe multiple industrial automation systems (e.g., 204).

In response to any changes to the industrial automation system 204(e.g., modification of settings of an industrial device, replacement ofan industrial asset, software update to an industrial device,modification of an industrial process), the analytics component 202 ormodeler component 222 can detect and/or receive information relating tothe changes to the industrial automation system 204. The analyticscomponent 202 or modeler component 222 can analyze the informationrelating to the changes to the industrial automation system 204. Basedat least in part on the results of the data analysis, the modelercomponent 222 can update the model 224 to generate a modified model(e.g., new model 224) that can reflect and incorporate the changes madeto the industrial automation system 204 to facilitate accuratelymodeling the industrial automation system 204 and improving operation ofthe industrial automation system 204. Also, based at least in part onthe results of the data analysis, the analytics management component 218can update (e.g., modify) correlations or generate new correlationsrelating to respective portions (e.g., industrial assets) or aspects ofthe industrial automation system 204, or update correlations or generatenew correlations between respective portions (e.g., industrial assets)or aspects of the industrial automation system 204 and extrinsicconditions or events, to facilitate improving operation of theindustrial automation system 204.

As disclosed, the modeler component 222 can generate the model 224 ofthe industrial automation system 204 based at least in part on theresults of the analysis of the industrial-automation-system-related databy the analytics component 202. The generated model 224 can be used bythe analytics component 202 to facilitate performing analytics on theindustrial automation system 204 and determining correlations relatingto respective portions (e.g., industrial assets) or aspects of theindustrial automation system 204, or correlations between respectiveportions (e.g., industrial assets) or aspects of the industrialautomation system 204 and extrinsic conditions or events. In someimplementations, the analysis performed by the analytics managementcomponent 218 can be based in part on the aggregation of data relatingto respective industrial assets (e.g., 210, 212, 214) and/ornetwork-related devices of the network component 216, wherein the dataaggregation can be modeled, in the model 224, on the physical structureof the industrial automation system 204. Such data aggregation andstructuring can allow (e.g., enable) the analytics management component218 to locate respective industrial assets (e.g., 210, 212, 214) and/ornetwork-related devices within the industrial-automation-system context(e.g., the analytics management component 218 can identify an asset, adevice, or production area in the industrial automation system 204 inwhich a sensor resides, based at least in part on the data aggregationand data structuring associated with the model 224.)

In accordance with other aspects and implementations of the disclosedsubject matter, once the model 224 of an industrial automation system204 is constructed, the model 224 can be an active part of theenterprise entity's industrial automation system 204 and can beintegrated with other services (e.g., analytics services, correlation ordata visualization services, virtualization services, custom dataservices, remote services) and applications.

Turning to FIG. 3, illustrated is a block diagram of an example system300 that can employs visualization tools or techniques that canfacilitate presenting analytics-related information relating to anindustrial automation system(s) to users, in accordance with variousimplementations and embodiments of the disclosed subject matter. Thesystem 300 can comprise an analytics component 302, an industrialautomation system(s) 304, a collection component 306, and a data store308, wherein the industrial automation system(s) 304 can compriseindustrial devices 310, industrial processes 312, other industrialassets 314, and a network component 316, and wherein the analyticscomponent 302 can comprise an analytics management component 318. Thesystem 300 also can comprise or be associated with a communicationdevice 320, which can be associated with the analytics component 302,industrial automation system(s) 304, collection component 306, and/ordata store 308. In some implementations, the analytics component 302,collection component 306, and/or data store 308 can reside in a cloudplatform that can be associated with (e.g., interfaced orcommunicatively connected to) the industrial automation system 304.

The analytics component 302 can comprise a visualization component 322that can generate and present (e.g., communicate, display) informationrelating to the operation of the industrial automation system 304 forviewing by a user, for example, via the communication device 320. Forinstance, the visualization component 322 can generate and presentinformation relating to the correlations determined or identified by theanalytics management component 318. The visualization component 322 canpresent the correlation-related information, or otherindustrial-automation-system-related information, in virtually anydesired form, such as, for example, as a set of data values (e.g.,customized data), a chart (e.g., a pie chart, a bar chart, adistribution chart, a geographical or logical location chart) or graph(e.g., line graph), a heat map (e.g., a graphical representation of datacomprising respective data values that can be represented by respectivecolors), a dashboard comprising correlation-related data, a visualdiagram of the industrial automation system, or portion thereof, withcorrelation-related data overlaid in proximity to the industrial asset(e.g., 310, 312, 314) or other device (e.g., of the network component316) associated with the correlation, a Venn diagram, or another type ofdata visualization.

The visualization component 322 also can generate and present therespective correlations relating to the industrial automation system 304in a desired ranked order (e.g., highest ranked priority correlation tolowest ranked priority correlation) or can emphasize correlations thathave higher priority (e.g., display higher priority correlations in alarger form than lower priority correlations, display higher prioritycorrelations using a different color than that of lower prioritycorrelations). The analytics management component 318 or visualizationcomponent 322 can determine and assign respective priority rankings forrespective correlations, based at least in part on the relativeimportance of the respective correlations with respect to each otherand/or the relative impact on a goal(s) (e.g., production goal and/orother goal) relating to the industrial automation system 304 withrespect to each other. The analytics management component 318 orvisualization component 322 can determine the relative importance of therespective correlations with respect to each other and/or the relativeimpact on a goal(s) relating to the industrial automation system 304with respect to each other based at least in part on the results of theanalysis of the industrial-automation-system-related data.

The visualization component 322 also can modify the visualization andpresentation of information relating to the operation of the industrialautomation system 304, for example, based at least in part on (e.g., inresponse to) a user (e.g., input information received from a user).Referring briefly to FIGS. 4 and 5 (along with FIG. 3), FIG. 4 presentsa diagram of an example information display 400 relating to theindustrial automation system 304, and FIG. 5 presents a diagram of anexample customized information display 500 (e.g., selected informationdisplay) relating to two or more selected items of interest in relationto the industrial automation system 304, in accordance with variousimplementations and embodiments of the disclosed subject matter. Forpurposes of this illustrative example, the visualization component 322can generate and present the information display 400 relating to theindustrial automation system 304 to the user (e.g., via thecommunication device 320). The information display 400 can relate tocertain aspects or portions (e.g., an industrial process 312, aproduction output of the industrial automation system 304 or portionthereof, or an extrinsic condition or event) of or associated with theindustrial automation system 304, for example, that can be relevant to awork task the user is performing with respect to the industrialautomation system 304. While, for purposes of this illustrative example,the information display 400 is not referred to as a customizedinformation display (wherein display 500 is referred to as a customizedinformation display), it is to be appreciated and understood that theinformation display 400 also can be a customized information display(which can be different from the customized information display 500),wherein the information display 400 can be customized by thevisualization component 322 based at least in part on an identifier, arole, an authentication credential, a user preference, or access rightsto the industrial-automation-system-related information associated withthe user and/or other factors, in accordance with defined analyticscriteria.

While the user is viewing the information display 400, the user maydesire to see any correlations between two or more items (e.g.,industrial asset, aspect, condition, event) of or associated with anindustrial automation system 304. In response to input informationselecting the two or more items (e.g., as received from thecommunication device 320 or as received via another user interface)and/or based at least in part on an identified role (e.g., operator,supervisor, technician, or other type of role) of the user with respectto the industrial automation system 304, the visualization component 322can modify the information visualization (e.g., information display 400)being presented to the user (e.g., via a user interface of thecommunication device 320 or the other user interface) to present (e.g.,display), to the user, a customized visualization (e.g., customizedinformation display 500) that can comprise information relating to thetwo or more selected items of interest, including information relatingto the respective correlations (if any) between the two or more items ofinterest selected by the user. As desired (e.g., when pertinent to theuser), the customized information display 500 generated and presented bythe visualization component 322 can convey (e.g., present) a statisticalrelationship of each selected item of interest (e.g., each selectedvariable) to a specified goal (e.g., product output, energy costs,inventory levels, revenue, etc.) associated with the industrialautomation system 304. The two or more items of interest can be orrelate to, for example, internal plant aspects (e.g., industrial asset(e.g., 310, 312, or 314), a network-related device of the networkcomponent 316, a production output or production goal relating to theindustrial automation system 304, an internal inventor of materials,downtime of a selected industrial device 310 or industrial process 312,or other internal condition or event within the industrial automationsystem 304), or extrinsic (e.g., external) events or conditions (e.g., aprice of raw materials or processed products (e.g., grain price), energycost for energy that is used in connection with production ordistribution of products, inventory of an external supplier ofmaterials, weather conditions that can have an effect on production oroperations associated with the industrial automation system 304, orother extrinsic events or conditions).

The visualization component 322 can generate and present the informationdisplay 400 and customized information display 500 to the user (e.g.,via the communication device 320) in virtually any desired (e.g.,optimal, acceptable, suitable) format. For example, the respectiveformats of visualization of the information display 400 and customizedinformation display 500 by the visualization component 322 can bedetermined by the visualization component 322 based at least in part onan identifier, a role, an authentication credential, a user preference,or access rights to the industrial-automation-system-related informationassociated with the user, one or more items of interest selected by theuser, and/or other factors, in accordance with the defined analyticscriteria. The format of visualization of an information display canrelate to, for example, the type of information presented, the amount ofinformation being presented, the type of information presentation (e.g.,bar graph, pie chart, list of data values, etc.) used, the type ofcorrelation being presented, or other factors.

In some implementations, the analytics management component 318 orvisualization component 322 can generate correlation rankings relatingto the industrial automation system 304 that can be specific to theparticular vertical or industry being analyzed. For example, one type ofenterprise (e.g., food and drug enterprise) may value certain types ofcorrelations differently than another type of enterprise (e.g.,automotive enterprise) values such certain types of correlations, basedat least in part on the respective goals and imperatives of therespective types of enterprises. In generating the correlation rankings,the analytics management component 318 or visualization component 322can tailor or customize the correlation rankings based at least in parton the respective goals and imperatives of the respective types ofenterprises. For instance, the analytics management component 318 orvisualization component 322 can rank a first type of correlation higherthan a second type of correlation with respect to a first type ofindustrial enterprise, in accordance with the goals and imperatives ofthe first type of enterprise, while ranking the second type ofcorrelation higher than the first type of correlation with respect to asecond type of industrial enterprise, in accordance with the goals andimperatives of the second type of enterprise.

The visualization component 322 also can modify the visualization andpresentation of information relating to the operation of the industrialautomation system 304 to enable a user to focus on and/or see moredetailed information regarding a particular portion of the informationbeing presented to the user, for example, based at least in part on(e.g., in response to) a user (e.g., input information received from auser). Referring briefly to FIGS. 6 and 7 (along with FIG. 3), FIG. 6presents a diagram of an example information display 600 relating to theindustrial automation system 304, and FIG. 7 presents a diagram of anexample customized information display 700 (e.g., selected informationdisplay) relating to selection of a portion of the information display(e.g., 600) in relation to the industrial automation system 304, inaccordance with various implementations and embodiments of the disclosedsubject matter. For purposes of this illustrative example, thevisualization component 322 can generate and present the informationdisplay 600 relating to the industrial automation system 304 to the user(e.g., via the communication device 320). The information display 600can relate to certain aspects or portions (e.g., an industrial device310, an industrial process 312, a production output associated with theindustrial automation system 304 or portion thereof, or an extrinsiccondition or event) of or associated with the industrial automationsystem 304, for example, that can be relevant to a work task the user isperforming with respect to the industrial automation system 304. While,for purposes of this illustrative example, the information display 600is not referred to as a customized information display (wherein display700 is referred to as a customized information display), it is to beappreciated and understood that the information display 600 also can bea customized information display (which can be different from thecustomized information display 700), wherein the information display 600can be customized by the visualization component 322 based at least inpart on an identifier, a role, an authentication credential, a userpreference, or access rights to the industrial-automation-system-relatedinformation associated with the user and/or other factors, in accordancewith defined analytics criteria.

The information display 600 of FIG. 6 can comprise a first informationportion 602 that can comprise a first subset of information relating tothe industrial automation system 304, and a second information portion604 that can comprise a second subset of information relating to theindustrial automation system 304. The first subset of information canbe, for example, an information summary, an abstract of information,alert data, notification data, relating to the portion(s) or aspect(s)of the industrial automation system 304 for which the first informationportion 602 is presenting information. The second subset of informationcan be, for example, a different information summary, a differentabstract of information, different alert data, different notificationdata, relating to the portion(s) or aspect(s) of the industrialautomation system 304 for which the second information portion 604 ispresenting information.

While the user is viewing the information display 600, the user maydesire to see more information (e.g., drill down further into the data)regarding some of the first subset of information being presented in thefirst information portion 602, for example, to gain more informationregarding the operations relating to the portion(s) or aspect(s) of theindustrial automation system 304 represented by the first informationportion 602. The visualization component 322 can enable the user todrill down into (e.g., to expose more detailed)industrial-automation-system-related data to a desired level ofgranularity.

For instance, in response to input information selecting an item(s) ofinformation on the first information portion 602 (e.g., as received fromthe communication device 320 or as received via another user interface)and/or based at least in part on an identified role (e.g., operator,supervisor, technician, or other type of role) of the user with respectto the industrial automation system 304, the visualization component 322can modify the information visualization (e.g., information display 600)being presented to the user (e.g., via the communication device 320 orthe other user interface) to present (e.g., display), to the user, acustomized visualization (e.g., customized information display 700) thatcan comprise a detailed (e.g., more detailed) information display 702that can provide (e.g., expose, present) additional informationregarding the selected item(s) of information in the first informationportion 602 that can be beyond the amount of information presented forthat item(s) of information in the first information portion 602.

For example, the first subset of information in the first informationportion 602 may provide a summary of production data for a first portion(e.g., a first industrial process 312) of the industrial automationsystem 304 over the course of a day. The user (e.g., a plant supervisor)can desire to drill down into the production data to find out moreinformation regarding the production data for the first portion of theindustrial automation system 304 on an hourly basis or shift basis. Theuser can select the production data summary via a user interface on thecommunication device 320 or another user interface. In response, thevisualization component 322 can generate and present the customizedinformation display 700 comprising the detailed information display 702,which can comprise additional information regarding the respectiveproduction data for the first portion of the industrial automationsystem 304 for each hour or each shift over the course of the day.

To facilitate presenting the additional information provided in thedetailed information display 702, as desired or as necessary, thevisualization component 322 can increase the amount of the displayscreen used to display the detailed information display 702, decreasethe amount of the display screen used to display other parts of thefirst subset of information associated with the first informationportion 602, de-emphasize the other parts of the first subset ofinformation associated with the first information portion 602, removethe other parts of the first subset of information associated with thefirst information portion 602, decrease the amount of the display screenused to display the first subset of information associated with thesecond information portion 604, de-emphasize the second subset ofinformation associated with the second information portion 604, and/orremove the other parts of the first subset of information associatedwith the second information portion 604, in accordance with the definedanalytics criteria and/or a user preference(s) of the user. In someimplementations, respective information display portions can bedisplayed in a picture-in-picture (PIP) format, wherein one informationdisplay portion can be displayed more prominently over most of a displayscreen of a user interface (e.g., of a communication device) and anotherinformation display portion can be displayed in a smaller portion of thedisplay screen within or in proximity to the larger portion of thedisplay screen that is displaying the more prominently displayedinformation portion.

FIG. 8 illustrates a block diagram of an example system 800 that cancapture video of operations of an industrial automation system tofacilitate performing analytics in connection with the industrialautomation system, in accordance with various implementations andembodiments of the disclosed subject matter. The system 800 can comprisean analytics component 802, an industrial automation system(s) 804, acollection component 806, and a data store 808, wherein the industrialautomation system(s) 804 can comprise industrial devices 810, industrialprocesses 812, other industrial assets 814, and a network component 816,and wherein the analytics component 802 can comprise an analyticsmanagement component 818. The system 800 also can comprise or beassociated with a communication device 820, which can be associated withthe analytics component 802, industrial automation system(s) 804,collection component 806, and/or data store 808. In someimplementations, the analytics component 802, collection component 806,and/or data store 808 can reside in a cloud platform that can beassociated with (e.g., interfaced or communicatively connected to) theindustrial automation system 804.

The system 800 can comprise a capture component 822 that can comprise aset of capture sub-components (e.g., cameras), including a first capturesub-component 824, second capture sub-component 826, and third capturesub-component 828. The set of capture sub-components (e.g., 824, 826,828) can be distributed at desired locations in the industrial facility830 to enable the respective capture sub-components (e.g., 824, 826,828) to capture video of respective portions of the industrialautomation system 804 to generate real-time video streams of therespective portions of the industrial automation system 804. Each of therespective portions of the industrial automation system 804 can compriseone or more industrial devices 810, one or more industrial processes812, one or more other industrial assets 814, one or morenetwork-related devices of the network component 816, and/or one or moreemployees.

To facilitate communication (e.g., migration) of the respective videostreams of the respective portions of the industrial automation system804 to the cloud platform, the respective capture sub-components (e.g.,824, 826, 828) of the set of capture sub-components can compriserespective cloud gateway components, including a first cloud gatewaycomponent 832, a second cloud gateway component 834, and a third cloudgateway component 836, that can enable the respective capturesub-components (e.g., 824, 826, 828) to communicate the respective videostreams to the cloud platform (e.g., to the analytics component 802,collection component 806, or other component in the cloud platform). Theanalytics component 802 can receive the respective video streams of therespective portions of the industrial automation system from therespective capture sub-components (e.g., 824, 826, 828) for analysis.

As part of the analysis of the respective video streams, the analyticsmanagement component 818 can perform pattern recognition analysis orother recognition analysis on the respective video streams to facilitatedetermining or learning (e.g., through system monitoring over time)patterns that can represent suitable or normal operation of theindustrial automation system 804 and/or patterns that can representabnormal operation of the industrial automation system 804. For example,the analytics management component 818 can analyze (e.g., using patternrecognition analysis) a video stream received from a capturesub-component (e.g., 824) and can detect or identify an operationalabnormality has occurred based at least in part on detecting oridentifying a deviation (e.g., a visual deviation) from a pattern thatcan represent suitable or normal operation of the industrial automationsystem 804. The deviation can be, for example, a bottle falling over ona conveyor associated with an industrial process 812, a leak from anindustrial device 810, smoke emanating from an industrial device 810, ora color change in a product or material that can indicate an operationalabnormality.

Based at least in part on the results of the analysis of the videoand/or other data, and/or in response to identifying a visual deviationin the operation of the industrial automation system 804, the analyticsmanagement component 818 can determine correlations between respectiveportions or aspects of the industrial automation system 804, between aportion or aspect of the industrial automation system 804 and anextrinsic event or condition, etc., or determine other analyticsrelating to the industrial automation system 804. The analyticsmanagement component 818 also can generate instructions,recommendations, or notifications relating to the correlations and/or tothe identified visual deviation in the operation of the industrialautomation system 804. The analytics management component 818 cancommunicate the instructions, recommendations, or notifications relatingto the correlations and/or to the identified visual deviation to theuser (e.g., via a user interface of the communication device 820 oranother user interface) or to the industrial automation system 804 forconsideration and/or action by the user or industrial automation system804. For example, if the deviation is a bottle that has fallen on theconveyor, the instruction, recommendation, or notification can comprise,for example, notifying a user that the bottle has fallen on the conveyorso that the user can pick up the bottle, recommend an adjustment to theconveyor or other industrial device to facilitate reducing the risk ofthe bottle falling on the conveyor, or instruct an industrial device toadjust its parameters to facilitate reducing the risk of the bottlefalling on the conveyor.

As disclosed herein, the systems (e.g., 100, 200, 300, 800) disclosedherein, or respective portions thereof, can be located in a cloudplatform. To provide a general context for the cloud-based systems(e.g., analytics systems, modeling systems, visualization systems) andservices described herein, FIG. 9 illustrates a block diagram of ahigh-level overview of an example industrial enterprise 900 that canleverage cloud-based services, including analytics services, modelingservices, visualization services, data collection services, and datastorage services, in accordance with various aspects and embodiments ofthe disclosed subject matter. The industrial enterprise 900 can compriseone or more industrial facilities, such as industrial facility₁ 904 ₁ upthrough industrial facility_(N) 904 _(N), wherein each industrialfacilitate can include a number of industrial devices in use. Forexample, industrial facility₁ 904 ₁ can comprise industrial device₁ 908₁ up through industrial device_(N) 908 _(N), and industrial facility_(N)904 _(N) can comprise industrial device₁ 910 ₁ up through industrialdevice_(N) 910 _(N). The industrial devices (e.g., 908 ₁, 908 _(N), 910₁, 910 _(N), etc.) can make up one or more industrial automation systemsthat can operate within the respective industrial facilities (e.g.,industrial facility₁ 904 ₁ up through industrial facility_(N) 904 _(N)).Exemplary industrial automation systems can include, but are not limitedto, batch control systems (e.g., mixing systems), continuous controlsystems (e.g., proportional-integral-derivative (PID) control systems),or discrete control systems. Industrial devices (e.g., 908 ₁, 908 _(N),910 ₁, 910 _(N), etc.) can include such industrial devices as industrialcontrollers (e.g., programmable logic controllers or other types ofprogrammable automation controllers); field devices such as sensors andmeters; motor drives; HMIs; industrial robots, barcode markers, andreaders; vision system devices (e.g., vision cameras); smart welders; orother types of industrial devices.

Exemplary industrial automation systems can include one or moreindustrial controllers that can facilitate monitoring and controlling oftheir respective industrial processes. The industrial controllers canexchange data with the field devices using native hardwired input/output(I/O) or via a plant network, such as Ethernet/Internet Protocol (IP),Data Highway Plus, ControlNet, Devicenet, or the like. A givenindustrial controller typically can receive any combination of digitalor analog signals from the field devices that can indicate a currentstate of the industrial devices and/or their associated industrialprocesses (e.g., temperature, position, part presence or absence, fluidlevel, etc.), and can execute a user-defined control program that canperform automated decision-making for the controlled industrialprocesses based on the received signals. The industrial controller canoutput appropriate digital and/or analog control signaling to the fielddevices in accordance with the decisions made by the control program.These outputs can include device actuation signals, temperature orposition control signals, operational commands to a machining ormaterial handling robot, mixer control signals, motion control signals,and the like. The control program can comprise any suitable type of codethat can be used to process input signals read into the controller andto control output signals generated by the industrial controller,including, but not limited to, ladder logic, sequential function charts,function block diagrams, structured text, or other such platforms.

Although the exemplary overview illustrated in FIG. 9 depicts theindustrial devices (e.g., 908 ₁, 908 _(N), 910 ₁, 910 _(N)) as residingin fixed-location industrial facilities (e.g., industrial facility₁ 904₁ up through industrial facility_(N) 904 _(N), respectively), in someimplementations, the industrial devices (e.g., 908 ₁, 908 _(N), 910 ₁,and/or 910 _(N)) also can be part of a mobile control and/or monitoringapplication, such as a system contained in a truck or other servicevehicle.

According to one or more embodiments of the disclosed subject matter,industrial devices (e.g., 908 ₁, 908 _(N), 910 ₁, 910 _(N), etc.) can becoupled to a cloud platform 902 to facilitate leveraging cloud-basedapplications and services (e.g., data collection services, data storageservices, analytics services, modeling services, visualization services,etc.) associated with the cloud platform 902. That is, the industrialdevices (e.g., 908 ₁, 908 _(N), 910 ₁, 910 _(N), etc.) can be configuredto discover and interact with cloud-based computing services 912 thatcan be hosted by the cloud platform 902. The cloud platform 902 can beany infrastructure that can allow the cloud services 912 (e.g.,cloud-based computing services, shared computing services) to beaccessed and utilized by cloud-capable devices. The cloud platform 902can be a public cloud that can be accessible via a public network, suchas the Internet, by devices having public network connectivity (e.g.,Internet connectivity) and appropriate authorizations to utilize thecloud services 912. In some scenarios, the cloud platform 902 can beprovided by a cloud provider as a platform-as-a-service (PaaS) and/orreliability-as-a-service (RaaS), and the cloud services 912 can resideand execute on the cloud platform 902 as a cloud-based service. In somesuch configurations, access to the cloud platform 902 and associatedcloud services 912 can be provided to customers as a subscriptionservice by an owner of the cloud services 912. Additionally and/oralternatively, the cloud platform 902 can be a private cloud that can beoperated internally by the industrial enterprise 900 or an associatedenterprise associated with a third-party entity. An exemplary privatecloud platform can comprise a set of servers that can host the cloudservices 912 and can reside on a private network (e.g., an intranet, acorporate network, etc.) that can be protected by a firewall.

The cloud services 912 can include, but are not limited to, datacollection, data storage, performing analytics on data, controlapplications (e.g., applications that can generate and deliver controlinstructions to industrial devices (e.g., 908 ₁, 908 _(N), 910 ₁, 910_(N), etc.) based at least in part on analysis of real-time or nearreal-time system data or other factors), determining correlationsbetween respective items of interest associated with an industrialautomation system(s), determining modifications that can be made inconnection with an industrial automation system(s) based at least inpart on results of the analytics and/or the determined correlations,remote monitoring and support, generation and management of a model(s)of an industrial automation system(s) that can correspond to theindustrial automation system(s), generation and management ofvisualizations of data associated with industrial automation system(s),remote control of an industrial automation system(s) via a model(s) orvirtualized industrial automation system(s), customization of a model(s)or virtualized industrial automation system and/or a data overlay on thevirtualized industrial automation system, generation of virtual notes,view sharing (e.g., sharing of customized view of, customized dataoverlay associated with, and/or a virtual note associated with, avirtualized industrial automation system), provision of security inconnection with a model or virtualized industrial automation system andan associated industrial automation system, or provision of otherapplications or services relating to industrial automation. If the cloudplatform 902 is a web-based cloud, industrial devices (e.g., 908 ₁, 908_(N), 910 ₁, 910 _(N), etc.) at the respective industrial facilities 904can interact with cloud services 912 via the public network (e.g., theInternet). In an exemplary configuration, the industrial devices (e.g.,908 ₁, 908 _(N), 910 ₁, 910 _(N), etc.) can access the cloud services912 through separate cloud gateways (e.g., cloud gateway component 906_(1M) up through cloud gateway component 906 _(NM)) at the respectiveindustrial facilities (e.g., industrial facility₁ 904 ₁ up throughindustrial facility_(N) 904 _(N), respectively), wherein the industrialdevices (e.g., 908 ₁, 908 _(N), 910 ₁, 910 _(N), etc.) can connect tothe respective cloud gateway components (e.g., cloud gateway component906 _(1M) up through cloud gateway component 906 _(NM)) through aphysical (e.g., wireline) or wireless local area network or radio link.In another exemplary configuration, the industrial devices (e.g., 908 ₁,908 _(N), 910 ₁, 910 _(N), etc.) can access the cloud platform 902directly using an integrated cloud gateway service. Cloud gatewaycomponents (e.g., cloud gateway component 906 _(1M) up through cloudgateway component 906 _(NM)) also can comprise an integrated componentof a network infrastructure device, such as a firewall box, router, orswitch.

Providing industrial devices with cloud capability via the cloud gatewaycomponents (e.g., cloud gateway component 906 _(1M) up through cloudgateway component 906 _(NM)) can offer a number of advantages particularto industrial automation. For instance, cloud-based storage (e.g.,cloud-based data store) offered by the cloud platform 902 can be easilyscaled to accommodate the large quantities of data that can be generateddaily by an industrial enterprise. Further, multiple industrialfacilities (e.g., industrial facility₁ 904 ₁ up through industrialfacility_(N) 904 _(N)) at different geographical locations can migrate(e.g., communicate) their respective industrial automation data to thecloud platform 902 (e.g., via the collection component) for aggregation,collation, collective big data analysis, and enterprise-level reportingwithout the need to establish a private network between the respectiveindustrial facilities. Industrial devices (e.g., 908 ₁, 908 _(N), 910 ₁,910 _(N), etc.) and/or cloud gateway components (e.g., cloud gatewaycomponent 906 _(1M) up through cloud gateway component 906 _(NM)) havingsmart configuration capability can be configured to automatically detectand communicate with the cloud platform 902 upon installation at anyfacility, which can thereby simplify integration with existingcloud-based data storage, analysis, or reporting applications used bythe industrial enterprise 900. In another exemplary application,cloud-based analytics applications (e.g., employed by an analyticssystem comprising the analytics component) can access the data relatingto an industrial automation system(s) stored in the cloud-based datastore, perform analytics on the data to generate analysis results,determine correlations between respective aspects (e.g., internal orintrinsic aspects, external or extrinsic aspects) associated with anindustrial automation system(s), and generate notifications,recommendations, and/or instructions based on the correlations tofacilitate improved operation of the industrial automation system(s).These industrial cloud-computing applications are only intended to beexemplary, and the systems and methods described herein are not limitedto these particular applications. As these examples demonstrate, thecloud platform 902, working with cloud gateway components (e.g., cloudgateway component 906 _(1M) up through cloud gateway component 906_(NM)), can allow builders of industrial applications to providescalable solutions as a service, removing the burden of maintenance,upgrading, and backup of the underlying infrastructure and framework.

FIG. 10 presents a block diagram of an exemplary analytics component1000 (e.g., cloud-based, or partially cloud-based, analytics component)according to various implementations and embodiments of the disclosedsubject matter. The analytics component 1000 can be part of an analyticssystem (e.g., a cloud-based analytics system). Aspects of the systems,apparatuses, or processes explained in this disclosure can constitutemachine-executable components embodied within machine(s), e.g., embodiedin one or more computer-readable mediums (or media) associated with oneor more machines. Such components, when executed by one or moremachines, e.g., computer(s), computing device(s), automation device(s),virtual machine(s), etc., can cause the machine(s) to perform theoperations described.

The analytics component 1000 can comprise a communication component 1002that can be used to communicate (e.g., transmit, receive) informationbetween the analytics component 1000 and other components (e.g.,communication devices, network-related devices, industrial devices,other types of industrial assets that have communication functionality,other devices with communication functionality that are associated withindustrial enterprises, cloud gateways, etc.). The information caninclude, for example, data relating to industrial automation systems,data relating to specifications, properties, or characteristics ofindustrial devices or other industrial assets, customer-related data,work-order-related data relating to work orders that will or may behandled by an industrial automation system, etc.

The analytics component 1000 can comprise an aggregator component 1004that can aggregate data received (e.g., obtained, collected, detected,etc.) from various entities (e.g., communication devices, industrialdevices, industrial assets, network-related devices, cloud gatewaycomponents, modeler component, virtualization component, other deviceswith communication functionality that are associated with industrialenterprises, processor component(s), user interface(s), data store(s),etc.). The aggregator component 1004 can correlate respective items ofdata based at least in part on type of data, source of the data, time ordate the data was generated or received, type of device or assetassociated with the data, identifier associated with a device or asset,customer associated with the data, user (e.g., operator, supervisor ormanager, engineer, technician, etc.) associated with the data,industrial automation system associated with the data, industrialenterprise associated with the system, etc., to facilitate processing ofthe data (e.g., analyzing of the data, determining correlations betweenitems of interest associated with the industrial automation system,generating a model of an industrial automation system, etc.).

The analytics component 1000 also can include a monitor component 1006that can monitor device data, process data, asset data, system data,customer data, and/or other data in connection with the industrialautomation systems. For instance, the monitor component 1006 can monitorinformation (e.g., signals, device or process statuses, networkcommunication of information, process flows, updates, modifications,etc.) associated with industrial automation systems, modeled industrialautomation systems, virtualized industrial automation systems,industrial enterprises, and/or systems or devices of customersassociated with the industrial enterprises to facilitate detectinginformation associated with industrial automation systems that canfacilitate performing analytics on such data, determining correlationsbetween respective items of interest associated with an industrialautomation system, generating visualizations (e.g., customizedinformation visualizations) of information relating to an industrialautomation system, determining recommendations or instructions based ondetermined correlations to facilitate improving operations associatedwith the industrial automation system, generating and updating models ofindustrial automation systems, generating and updating virtualizedindustrial automation systems, remotely tracking operation of orcontrolling operation of an industrial automation system via anassociated model or associated virtualized industrial automation system,customizing a view of and/or a data overlay associated with a model or avirtualized industrial automation system for a user, sharing a view(e.g., a customized view) of and/or a data overlay (e.g., a customizeddata overlay) associated with a model or a virtualized industrialautomation system with a communication device of another user,generating virtual notes in connection with a virtualized industrialautomation system, controlling and/or enforcing the viewability scopeassociated with a virtual note, and/or performing other services (e.g.,cloud-based services). The monitor component 1006 can be associated withsensors, meters, HMIs, communication monitoring components, or othercomponents associated with industrial automation systems, industrialenterprises, and/or systems or devices of the customers to facilitatethe monitoring of the industrial automation systems, industrialenterprises, and/or systems or devices of the customers.

The analytics component 1000 can comprise a detector component 1008 thatcan detect desired information associated with industrial automationsystems that can facilitate performing analytics-related services,visualization-related services, model-related services, andvirtualization-related services in connection with an industrialautomation system (e.g., performing analytics on data relating toindustrial automation systems, determining correlations betweenrespective items of interest relating to an industrial automationsystem(s), generating recommendations or instructions relating to thedetermined correlations between respective items of interest, generatingor updating a model, generating or updating a virtualized industrialautomation system, remotely interacting with (e.g., monitoring,tracking, and/or controlling, etc., operation of) an industrialautomation system via interacting with a model or a virtualizedindustrial automation system, etc.), in accordance with the definedanalytics criteria, defined modeling criteria, defined virtualizationcriteria, or other operation criteria. For instance, the detectorcomponent 1008 can detect or discover desired device data, process data,asset data, system data, and/or customer data in connection with theindustrial automation systems that can facilitate performing analyticson data relating to industrial automation systems, determiningcorrelations between respective items of interest relating to anindustrial automation system(s), generating recommendations orinstructions relating to the determined correlations between respectiveitems of interest, generating a model or a virtualized industrialautomation system that can accurately represent and/or interface with anindustrial automation system, remotely interacting with and/orcontrolling an industrial automation system via an associated model orvirtualized industrial automation system, and/or performing otheranalytics-related, visualization-related, model-related orvirtualization-related services or functions.

The analytics component 1000 also can include a collection component1010 that can receive, collect, or obtain data (e.g., desired devicedata, process data, asset data, system data, and/or customer data) fromindustrial automation systems, communication devices, models,virtualized industrial automation systems, extrinsic sources, etc., tofacilitate performing analytics-related, visualization-related,model-related and virtualization-related services, as more fullydisclosed herein. The data collected by the collection component 1010can be stored in the data store 1032, and/or can be made available toother components (e.g., analytics management component 1016, analyzercomponent 1018, etc.) to facilitate performing analytics on datarelating to industrial automation systems, determining correlationsbetween respective items of interest relating to an industrialautomation system(s), generating recommendations or instructionsrelating to the determined correlations between respective items ofinterest, generating and updating models of industrial automationsystems, generating and updating virtualized industrial automationsystems, remotely interacting with (e.g., monitoring, tracking, and/orcontrolling, etc.) an industrial automation system via an associatedmodel or virtualized industrial automation system, and/or performingother analytics-related, visualization-related, model-related, orvirtualization-related services or functions.

The analytics component 1000 can comprise an interface component 1012that can be employed to facilitate interfacing the analytics component1000 (or interfacing an associated modeler component or virtualizationcomponent) with industrial automation systems and their constituentcomponents (e.g., industrial devices or assets, network-related devicesor assets, etc.) or processes, systems or devices associated withcustomers, systems or devices associated with device manufacturers, etc.For instance, the interface component 1012 can be configured to receiveindustrial data (e.g., device data, process data, asset data, systemdata, configuration data, status data, process variable data, etc.) sentby one or more cloud-capable industrial devices, cloud gatewaycomponents, communication devices, or other sources of industrial data.The interface component 1012 also can be configured to receivenetwork-related data (e.g., data relating to communication conditions,network-status data, data identifying network-related devices, etc.)communicated by one or more network-related devices of the networkcomponent of an industrial automation system. The interface component1012 also can be configured to interface a model (or an virtualizedindustrial automation system) with a corresponding industrial automationsystem to facilitate remotely interacting with (e.g., monitoring,tracking, and/or controlling, etc., operation of) the industrialautomation system via interactions (e.g., user interactions) with themodel (or the virtualized industrial automation system (e.g., viavirtualized control of the virtualized operation of the virtualizedindustrial automation system)). The interface component 1012 further canbe configured to exchange data with one or more client or customerdevices via an Internet connection. For example, the interface component1012 can receive customer profile data, requests for firmware upgrades,customer service selections, information relating to work orders forproducts, customer preferences or requirements with regard to a workorder, or other such information from a client device (e.g., anInternet-capable client device, such as a phone, a computer, anelectronic tablet or pad, or other suitable Internet-capable device).The interface component 612 also can deliver upgrade notifications,firmware upgrades, reports or notifications regarding the evaluation ofand determinations regarding proposed modifications to an industrialautomation system, notifications of impending device failures,identification of asset or system inefficiencies, configurationrecommendations, or other such data to the client device.

The analytics component 1000 also can contain a controller component1014 that can control operations relating to processing data, performinganalytics on data, visualizing information relating to an industrialautomation system, determining correlations between respective items ofinterest associated with the industrial automation system, determiningand generating recommendations or instructions relating to thedetermined correlations between the respective items of interestassociated with the industrial automation system, facilitatinggenerating or updating model an industrial automation system,facilitating remotely controlling an industrial automation system (e.g.,using an associated model or virtualized industrial automation system),facilitating performing simulation operations using a model (e.g.,simulation model) in connection with an industrial automation system,and/or performing other operations in connection with the industrialautomation system. The controller component 1014 can facilitatecontrolling operations being performed by various components of theanalytics component 1000, controlling data flow between variouscomponents of the analytics component 1000, controlling data flowbetween the analytics component 1000 and other components or systemsassociated with the analytics component 1000, etc.

The analytics component 1000 also can comprise an analytics managementcomponent 1016 that can perform analytics on data, generatevisualizations of information relating to an industrial automationsystem, determine correlations between respective items of interestassociated with the industrial automation system, determine and generaterecommendations or instructions relating to the determined correlationsbetween the respective items of interest associated with the industrialautomation system, facilitate generating and/or updating a model thatcan represent an industrial automation system, facilitate remotelyinteracting with and/or controlling an industrial automation systemusing an associated model or virtualized industrial automation system,facilitate performing simulation operations using a model of anindustrial automation system, and/or performing other operations.

The analytics management component 1016 can comprise an analyzercomponent 1018 that can analyze data (e.g., device data, process data,asset data, system data, customer data, user-generated or user-provideddata, and/or other data) to facilitate performing analytics on data,visualizing information relating to an industrial automation system,determining correlations between respective items of interest associatedwith the industrial automation system, determining and generatingrecommendations or instructions relating to the determined correlationsbetween the respective items of interest associated with the industrialautomation system, generating or updating a model of an industrialautomation system, performing simulation of operation of an industrialautomation system using a model, etc. The analyzer component 1018 canparse data to facilitate identifying data that is relevant to performingan operation (e.g., performing analytics on data, visualizinginformation relating to an industrial automation system, determiningcorrelations between respective items of interest associated with theindustrial automation system, determining and generating recommendationsor instructions relating to the determined correlations between therespective items of interest, etc.) by the analytics component 1000.Based at least in part on the analysis of the data, the analyzercomponent 1018 can generate analysis results that can be provided toanother component (e.g., processor component 1030, data store 1032,etc.) to facilitate the performance of various operations by theanalytics component 1000.

The analytics management component 1016 can include a correlationcomponent 1020 that can determine correlations between respective itemsof interest associated with an industrial control system based at leastin part on analytics results, in accordance with the set of definedanalytics criteria, as more fully disclosed herein. For example, basedat least in part on the analytics results, the correlation component1020 can determine a correlation between a first item of interestassociated with an industrial control system (e.g., production output ofan industrial automation system) and a second item of interestassociated with the industrial automation system (e.g., employeeinteraction with (e.g., performance of work tasks in connection with)the industrial automation system).

The analytics management component 1016 also can comprise arecommendation/instruction component 1022 that can determine andgenerate one or more recommendations or instructions that can specifyone or more changes that can be made in connection with an industrialautomation system, for example, based at least in part on one or moredetermined correlations between respective items of interest associatedwith an industrial automation system, to facilitate improving operationsor performance associated with the industrial automation system. Forexample, in response to a determination that there is a correlationbetween a first item of interest associated with the industrialautomation system (e.g., industrial device, such as a motor) and asecond item of interest associated with the industrial automation system(e.g., production output from a conveyor), wherein it is determined thata current parameter for the industrial device is causing an undesirable(e.g., sub-optimal, unacceptable) production output from the conveyor,the recommendation/instruction component 1022 can determine and generateinstructions that can facilitate changing (e.g., adjusting, modifying)the parameter of the industrial device to a new parameter setting thatcan result in desired improvement in the production output from theconveyor. As another example, in response to a determination that thereis a correlation between a first item of interest associated with theindustrial automation system (e.g., an employee's performance of certainwork tasks in connection with the industrial automation system) and asecond item of interest associated with the industrial automation system(e.g., production output from a production line on which the employeeworks), wherein it is determined that the employee's performance of worktasks is causing an undesirable (e.g., sub-optimal, unacceptable)production output from the production line, therecommendation/instruction component 1022 can determine and generate arecommendation that can recommend that the employee change (e.g.,modify) how the employee performs the certain work tasks or recommend achange in work assignments for that employee and another employee tofacilitate improving the production output from the production line. Therecommendations or instructions generated by therecommendation/instruction component 1022 can be communicated to acommunication device of the user (e.g., the employee, or supervisor ofthe employee), or to another desired destination (e.g., the industrialdevice to facilitate changing the parameter based on the instructions).

The analytics management component 1016 can contain a visualizationcomponent 1024 that can generate and present (e.g., communicate,display) information relating to the operation of an industrialautomation system for viewing by a user, for example, via acommunication device. For instance, the visualization component 1024 cangenerate and present information (e.g., in a multi-dimensionalvisualization of an information display) relating to the correlationsbetween respective items of interest associated industrial automationsystem that have been determined or identified by the analyticsmanagement component 1016. The visualization component 1024 can presentthe correlation-related information, or otherindustrial-automation-system-related information, in virtually anydesired format (e.g. 2-D or 3-D format), such as, for example, as a setof data values (e.g., customized data), a chart (e.g., a pie chart, abar chart) or graph (e.g., line graph), a heat map, a dashboardcomprising correlation-related data, a visual diagram of the industrialautomation system, or portion thereof, with correlation-related dataoverlaid in proximity to the industrial asset or other device associatedwith the correlation, Venn diagram, or other type of informationvisualization. The visualization component 1024 also can determine andpresent (e.g., generate a visualization of) respective rankings (e.g.,priority rankings) of respective correlations between respective itemsof interest associated with an industrial automation system, wherein thevisualization component 1024 can generate a visualization of aninformation display in a desired format to present (e.g., display,illustrate) the respective correlations between respective items ofinterest associated with an industrial automation system in a desiredorder (e.g., in order of the highest priority correlation to the lowestpriority correlation).

For respective users, the visualization component 1024 also cancustomize visualizations of information in an information display (e.g.,customized information display) for a user based at least in part onrespective items of interest associated with the industrial automationsystem in response to selection of the respective items of interest bythe user, and/or based at least in part on an identifier, a role, anauthentication credential, a user preference, access rights to theindustrial-automation-system-related information associated with theuser, and/or other factors, in accordance with the defined analyticscriteria. For example, the visualization component 1024 initially cangenerate an information display that can display certain informationrelating to the industrial automation system in a desired visualizationformat (e.g., a desired 2-D or 3-D information visualization format),wherein the information display can be displayed on a display screen ofa communication device of the user. The user may desire to viewinformation relating to certain items of interest to the user withrespect to the industrial automation system. Using the communicationdevice, the user can input information to facilitate selecting two ormore items of interest to the user. The communication device cancommunicate the input information relating to the two or more items ofinterest to the visualization component. Based at least in part on inputinformation, the analytics management component 1016 can performanalytics on data relating to the two or more items of interestassociated with the industrial automation system, and can determine oneor more correlations between the two or more items of interest based atleast in part on the analytics results. The visualization component 1024can generate a customized information display in a desired visualizationformat, wherein the customized information display can comprise customvisualized information relating to the one or more correlations betweenthe two or more items of interest associated with the industrialautomation system. The analytics component 1000 (e.g., via thecommunication component 1002) can communicate the customized informationdisplay to the communication device of the user, wherein the customizedinformation display can be presented (e.g., displayed) on the displayscreen of the communication device for viewing by the user.

The analytics management component 1016 also can include a filtercomponent 1026 that can facilitate filtering data to generate a subsetof data (e.g., correlation information) for presentation to a user(e.g., via a customized information display), for example, in responseto the user selecting two or more items of interest associated with theindustrial automation system. The filter component 1026 can employ oneor more different types of filters that can be used to filter datarelating to the industrial automation system, wherein the filters canfacilitate customizing, augmenting, and/or filtering data to provideusers with personalized or customized visualizations of informationassociated with the industrial automation system, as more fullydisclosed herein.

The analytics management component 1016 also can comprise a securitycomponent 1028 that can facilitate securing data associated withindustrial automation systems, customer data, models or virtualizationsof industrial automation systems, and industrial automation systems. Thesecurity component 1028 can facilitate controlling access to dataassociated with industrial automation systems, customer data, a model(or a particular (e.g., customized) view of a model), a virtualizedindustrial automation system (or a particular (e.g., customized) view ofa virtualized industrial automation system), and/or an associatedindustrial automation system (e.g., via the model or virtualizedindustrial automation system), based at least in part on respectiveauthentication credentials of respective users, respective access rightsof users, respective locations of users, etc., as more fully disclosedherein.

The analytics component 1000 also can comprise a processor component1030 that can operate in conjunction with the other components (e.g.,communication component 1002, aggregator component 1004, monitorcomponent 1006, etc.) to facilitate performing the various functions andoperations of the analytics component 1000. The processor component 1030can employ one or more processors (e.g., CPUs, GPUs, FPGAs, etc.),microprocessors, or controllers that can process data, such asindustrial data (e.g., device data, process data, asset data, systemdata, etc.) associated with industrial control systems, customer orclient related data, data relating to parameters associated with theanalytics component 1000 and associated components, etc., to facilitatedetermining correlations between respective items of interest associatedwith an industrial automation system(s), visualizing informationrelating to the industrial automation system(s) for a user, determiningchanges to operations or industrial assets associated with theindustrial automation system(s) that can facilitate improving operationsassociated with the industrial automation system(s) and/or achievingdesired goals with respect to the industrial automation system(s),and/or determining and providing notifications, recommendations, orinstructions relating to the correlations between the respective itemsof interest or the determined changes to operations or industrial assetsassociated with the industrial operating system(s), generating orupdating a model or virtualization that can represent an industrialautomation system, remotely interacting with and/or controlling anindustrial automation system using an associated model or virtualizedindustrial automation system, generating a simulation model of anindustrial automation system, performing simulation operations usingsimulation models performing other analytics-related,visualization-related, model-related, or virtualization-relatedoperations; and can control data flow between the analytics component1000 and other components associated with the analytics component 1000.

In yet another aspect, the analytics component 1000 can contain a datastore 1032 that can store data structures (e.g., user data, metadata);code structure(s) (e.g., modules, objects, classes, procedures),commands, or instructions; industrial data or other data associated withindustrial automation systems or industrial enterprises; customer orclient related information; data relating to analytics-related,visualization-related, model-related, or virtualization-related servicesin connection with industrial automation systems; parameter data;algorithms (e.g., algorithm(s) relating to performing analytics,determining correlations between respective items of interest associatedwith an industrial automation system, generating visualizations of data,ranking correlations in order of priority; algorithm(s) relating togenerating or updating model or a virtualized industrial automationsystem that can represent an industrial automation system, including itsindustrial devices, industrial processes, industrial assets,network-related devices, interrelationships between such devices,processes, or assets; or algorithm(s) relating to remotely interactingwith (e.g., monitoring, tracking, controlling, etc.) an industrialautomation system using an associated model or virtualized industrialautomation system); defined analytics criteria; defined modelingcriteria; defined virtualization criteria; other operation criteria; andso on. In an aspect, the processor component 1030 can be functionallycoupled (e.g., through a memory bus) to the data store 1032 in order tostore and retrieve data desired to operate and/or confer functionality,at least in part, to the communication component 1002, aggregatorcomponent 1004, monitor component 1006, etc., of the analytics component1000 and/or substantially any other operational aspects of the analyticscomponent 1000. It is to be appreciated and understood that the variouscomponents of the analytics component 1000 can communicate data,instructions, or signals between each other and/or between othercomponents associated with the analytics component 1000 as desired tocarry out operations of the analytics component 1000. It is to befurther appreciated and understood that respective components (e.g.,communication component 1002, aggregator component 1004, monitorcomponent 1006, etc.) of the analytics component 1000 each can be astand-alone unit, can be included within the analytics component 1000(as depicted), can be incorporated within another component of theanalytics component 1000 (e.g., within the analytics managementcomponent 1016) or a component separate from the analytics component1000, and/or virtually any suitable combination thereof, as desired. Italso is to be appreciated and understood that respective components(e.g., communication component 1002, aggregator component 1004, monitorcomponent 1006, . . . processor component 1030, data store 1032) of theanalytics component 1000 can be shared with and used by anothercomponent(s) (e.g., modeler component, virtualization component) orsystem(s) (e.g., modeler system, virtualization system) or such othercomponent(s) or system(s) can comprise components that can be the sameas or similar to that of the analytics component 1000.

In accordance with various embodiments, one or more of the variouscomponents of the analytics component 1000 (e.g., communicationcomponent 1002, aggregator component 1004, monitor component 1006, etc.)can be electrically and/or communicatively coupled to one another toperform one or more of the functions of the analytics component 1000. Insome implementations, one or more components of the analytics component1000 (e.g., communication component 1002, aggregator component 1004,monitor component 1006, . . . , analytics management component 1016) cancomprise software instructions that can be stored in the data store 1032and executed by the processor component 1030. The analytics component1000 also can interact with other hardware and/or software componentsnot depicted in FIG. 10. For example, the processor component 1030 caninteract with one or more external user interface devices, such as akeyboard, a mouse, a display monitor, a touchscreen, or other suchinterface devices.

FIG. 11 illustrates a diagram of an example system 1100 that canfacilitate performing analytics on data or generation of a model thatcan be representative of the industrial automation system, and theperformance of other analytics-related or model-related services basedat least in part collection of customer-specific industrial data by acloud-based analytics system or modeling system, in accordance withvarious aspects and embodiments of the disclosed subject matter. Thesystem 1100 can include an analytics system 1102 and a modeler system1104 that respectively can execute as cloud-based services on a cloudplatform (e.g., cloud platform 902 of FIG. 9), and can collect data frommultiple industrial automation systems, such as industrial automationsystem₁ 1106 ₁, industrial automation system₂ 1106 ₂, and/or (upthrough) industrial automation system_(N) 1106 _(N). The industrialautomation systems (e.g., 1106 ₁, 1106 ₂, 1106 _(N)) can comprisedifferent industrial automation systems within a given facility and/ordifferent industrial facilities at diverse geographical locations.Industrial automation systems (e.g., 1106 ₁, 1106 ₂, 1106 _(N)) also cancorrespond to different business entities (e.g., different industrialenterprises or customers), wherein the analytics system 1102 or modelersystem 1104 can collect and maintain a distinct customer data store 1108for each customer or business entity.

The analytics system 1102 or modeler system 1104 can organizemanufacturing data collected from the industrial automation systems(e.g., 1106 ₁, 1106 ₂, 1106 _(N)) according to various classes. In theillustrated example, manufacturing data can be classified according todevice data 1110, process data 1112, asset data 1114, and system data1116.

Referring briefly to FIG. 12, FIG. 12 illustrates a diagram of anexample hierarchical relationship 1200 between these example dataclasses. A given plant or supply chain 1202 can comprise one or moreindustrial automation systems 1204. The industrial automation systems1204 can represent the production lines or productions areas within agiven plant facility or across multiple facilities of a supply chain.Each industrial automation system 1204 can comprise a number of assets1206 that can represent the machines and equipment that make up theindustrial automation system (e.g., the various stages of a productionline). In general, each asset 1206 can comprise one or more industrialdevices 1208, which can include, for example, the programmablecontrollers, motor drives, HMIs, sensors, meters, etc. comprising theasset 1206. The various data classes depicted in FIGS. 11 and 12 areonly intended to be exemplary, and it is to be appreciated that anyorganization of industrial data classes maintained by the analyticssystem 1102 or modeler system 1104 is within the scope of one or moreembodiments of the disclosed subject matter.

Returning again to FIG. 11 (along with FIG. 12), the analytics system1102 or modeler system 1104 can collect and maintain data from thevarious devices and assets that make up the industrial automationsystems 1204 and can classify the data according to the aforementionedclasses for the purposes of facilitating analysis of the data,generation of models of the industrial automation systems (e.g., 1106 ₁,1106 ₂, 1106 _(N)), and/or performing other operations by the analyticssystem 1102 or modeler system 1104. Device data 1110 can comprisedevice-level information relating to the identity, configuration, andstatus of the respective devices comprising the industrial automationsystems (e.g., 1106 ₁, 1106 ₂, 1106 _(N)), including but not limited todevice identifiers, device statuses, current firmware versions, healthand diagnostic data, device documentation, identification andrelationship of neighboring devices that interact with the device, etc.

The process data 1112 can comprise information relating to one or moreprocesses or other automation operations carried out by the devices;e.g., device-level and process-level faults and alarms, process variablevalues (speeds, temperatures, pressures, etc.), and the like.

The asset data 1114 can comprise information generated, collected,determined, or inferred based on data that can be aggregated fromvarious (e.g., multiple) industrial devices over time, which can yieldhigher asset-level views of the industrial automation systems (e.g.,1106 ₁, 1106 ₂, 1106 _(N)). Example asset data 1114 can includeperformance indicators (KPIs) for the respective assets, asset-levelprocess variables, faults, alarms, etc. Since the asset data 1114 canyield a relatively longer term view of asset characteristics relative tothe device and process data, the analytics system 1102 or modeler system1104 can leverage the asset data 1114 to facilitate identifyingoperational patterns and correlations unique to each asset, among othertypes of analysis, and this can facilitate generating performanceanalytics, determining correlations between respective aspects (e.g.,internal or intrinsic aspects, external or extrinsic aspects) associatedwith an industrial automation system(s), generating notifications,recommendations, or instructions relating to the determinedcorrelations, generating respective modeling assets or virtualizationassets that can correspond to the respective assets, and generating,updating, using, customizing, etc., of model or a virtualized industrialautomation system of the industrial control system based at least inpart on the respective models or virtualizations of the respectiveassets associated with the industrial control system.

The system data 1116 can comprise collected, determined, or inferredinformation that can be generated based on data that can be aggregatedfrom various (e.g., multiple) assets over time. The system data 1116 cancharacterize system behavior within a large system of assets, yielding asystem-level view of each of the industrial automation systems (e.g.,1106 ₁, 1106 ₂, 1106 _(N)). The system data 1116 also can document theparticular system configurations in use and industrial operationsperformed at each of the industrial automation systems (e.g., 1106 ₁,1106 ₂, 1106 _(N)). For example, the system data 1116 can document thearrangement of assets, interconnections between devices, the productbeing manufactured at a given facility, an industrial process performedby the assets, a category of industry of each industrial system (e.g.,automotive, oil and gas, food and drug, marine, textiles, etc.), orother relevant information. Among other functions, this data can beaccessed by technical support personnel during a support session so thatparticulars of the customer's unique system and device configurationscan be obtained without reliance on the customer to possess completeknowledge of their assets.

As an example, a given industrial facility can include a packaging line(e.g., the system), which in turn can comprise a number of individualassets (e.g., a filler, a labeler, a capper, a palletizer, etc.). Eachasset can comprise a number of devices (e.g., controllers, variablefrequency drives, HMIs, etc.). Using an architecture similar to thatdepicted in FIG. 9, the analytics system 1102 or modeler system 1104 cancollect industrial data from the individual devices during operation andcan classify the data in the customer data store 1108 according to theaforementioned classifications. Note that some data may be duplicatedacross more than one class. For example, a process variable classifiedunder process data 1112 also can be relevant to the asset-level view ofthe system represented by the asset data 1114. Accordingly, such processvariables can be classified under both classes. Moreover, subsets ofdata in one classification can be derived, determined, or inferred basedon data under another classification. For example, subsets of systemdata 1116 that can characterize certain system behaviors can be derived,determined, or inferred based on a long-term analysis of data in thelower-level classifications.

In addition to maintaining the data classes (e.g., 1110, 1112, 1114,1116), each customer data store also can maintain a customer model 1118that can contain data specific to a given industrial entity or customer.The customer model 1118 can contain customer-specific information andpreferences, which can be leveraged by (e.g., used by) the analyticssystem 1102 or modeler system 1104 to facilitate generating or updatinga model of an industrial automation system, generating or updating avirtualized industrial automation system that can represent anindustrial automation system, remotely interacting with (e.g.,monitoring, tracking, controlling, etc.) an industrial automation systemusing an associated virtualized industrial automation system,customizing a view of and/or a data overlay associated with avirtualized industrial automation system for a user, sharing acustomized view of and/or a customized data overlay associated with avirtualized industrial automation system for a user, processing virtualnotes, generating a simulation model of an industrial automation system,performing simulation operations using simulation models, and/orperforming other operations in connection with the industrial automationsystem, etc. Example information that can be maintained in the customermodel 1118 can include a client identifier, client preferences orrequirements with regard to production or work orders associated with anindustrial automation system, analytics results relating to analysis ofdata associated with a client, determined correlations relating to anindustrial automation system(s), determined notifications,recommendations, and/or instructions relating to the determinedcorrelations, client contact information specifying which plantpersonnel are to be notified in response to results of a response of theindustrial automation system to a user interaction with an associatedmodel or virtualized industrial automation system, notificationpreferences that can specify how plant personnel are to be notified(e.g., email, mobile phone, text message, etc.), service contracts thatare active between the customer and the technical support entity, andother such information. The analytics system 1102 or modeler system 1104can marry (e.g., associate, link, unite, map, etc.) data collected foreach customer with the corresponding customer model 1118 foridentification and event handling purposes.

As noted above, industrial data can be migrated (e.g., communicated)from industrial devices to the cloud platform (e.g., comprising theanalytics system 1102 and modeler system 1104) using cloud gatewaycomponents. To this end, some devices can include integrated cloudgateways that can directly interface each device to the cloud platform.Additionally or alternatively, some configurations can utilize a cloudproxy device that can collect industrial data from multiple devicesassociated with the industrial automation systems (e.g., 1106 ₁, 1106 ₂,1106 _(N)) and can send (e.g., transmit) the data to the cloud platform.Such a cloud proxy can comprise a dedicated data collection device, suchas a proxy server that can share a network (e.g., communication network)with the industrial devices. Additionally or alternatively, the cloudproxy can be a peer industrial device that can collect data from otherindustrial devices.

FIGS. 13 and 14 depict block diagrams of example systems 1300 and 1400,respectively, illustrating respective techniques that can facilitatemigrating industrial data to the cloud platform via proxy devices forclassification and analysis by an analytics system (e.g., comprising ananalytics component) and a modeler system (e.g., comprising a modelercomponent), in accordance with various aspects and implementations ofthe disclosed subject matter. FIG. 13 depicts system 1300 that can beconfigured to comprise an industrial device that can act or operate as acloud proxy for other industrial devices of an industrial automationsystem. The industrial automation system can comprise a plurality ofindustrial devices, including industrial device₁ 1306 ₁, industrialdevice₂ 1306 ₂, industrial device₃ 1306 ₃, and/or (up through)industrial device_(N) 1306 _(N), that collectively can monitor and/orcontrol one or more controlled processes 1302. The industrial devices1306 ₁, 1306 ₂, 1306 ₃, and/or (up through) 1306 _(N) respectively cangenerate and/or collect process data relating to control of thecontrolled process(es) 1302. For industrial controllers such as PLCs orother automation controllers, this can include collecting data fromtelemetry devices connected to an industrial controller's I/O,generating data internally based on measured process values, etc.

In the configuration depicted in FIG. 13, industrial device₁ 1306 ₁ canact, operate, or function as a proxy for industrial devices 1306 ₂, 1306₃, and/or (up through) 1306 _(N), whereby the data 1314 from devices1306 ₂, 1306 ₃, and/or (up through) 1306 _(N) can be sent (e.g.,transmitted) to the cloud via proxy industrial device₁ 1306 ₁.Industrial devices 1306 ₂, 1306 ₃, and/or (up through) 1306 _(N) candeliver their respective data 1314 to the proxy industrial device₁ 1306₁ over the plant network or backplane 1312 (e.g., a Common IndustrialProtocol (CIP) network or other suitable network protocol). Using such aconfiguration, as desired, one industrial device can be interfaced tothe cloud platform (via cloud gateway component 1308). In someembodiments, the cloud gateway component 1308 can perform preprocessingon the gathered data prior to migrating the data to the cloud platform(e.g., time stamping, filtering, formatting, normalizing, summarizing,compressing, etc.). The collected and processed data can be pushed(e.g., transmitted) to the cloud platform as cloud data 1304 via cloudgateway component 1308. Once migrated to the cloud platform, thecloud-based analytics system or modeler system can classify the dataaccording to the example classifications described herein and/or canutilize the data to facilitate performing various operations relating todetermining respective correlations relating to respective items ofinterest associated with an industrial automation system(s), generatingnotifications, recommendations, and/or instructions relating tocorrelations between respective items of interest associated with theindustrial automation system(s) to facilitate improving operationsassociated with the industrial automation system(s) and/or achievingdefined goals associated with the industrial automation system(s),generating desired visualizations (e.g., visualized informationdisplays) relating to the industrial automation system(s) (e.g.,visualized information displays that can be customized based onrespective items of interest selected by a user), generating or updatingmodels of industrial automation systems, generating or updatingvirtualized industrial automation systems and using virtualizedindustrial automation systems (e.g., to facilitate remotely interactingwith and/or controlling operation of associated industrial automationsystems).

While the proxy device illustrated in FIG. 13 is depicted as anindustrial device that itself can perform monitoring, tracking, and/orcontrolling of a portion of controlled process(es) 1302, other types ofdevices also can be configured to serve as cloud proxies for multipleindustrial devices according to one or more implementations of thedisclosed subject matter. For example, FIG. 14 illustrates an examplesystem 1400 that can comprise a firewall box 1412 that can serve as acloud proxy for a set of industrial devices 1406 ₁, 1406 ₂, and/or (upthrough) 1406 _(N). The firewall box 1412 can act as a networkinfrastructure device that can allow the plant network 1416 to access anoutside network such as the Internet, while also providing firewallprotection that can prevent unauthorized access to the plant network1416 from the Internet. In addition to these firewall functions, thefirewall box 1412 can include a cloud gateway component 1408 that caninterface the firewall box 1412 with one or more cloud-based services(e.g., analytics services, visualization services (e.g., services forinformation visualization and customization), model-related services,virtualization-related services, data collection services, data storageservices, etc.). In a similar manner to the proxy industrial device 1306₁ of FIG. 13, the firewall box 1412 of FIG. 14 can collect industrialdata 1414 from including industrial device₁ 1406 ₁, industrial device₂1406 ₂, and/or (up through) industrial device_(N) 1406 _(N), which canmonitor and control respective portions of controlled process(es) 1402.Firewall box 1412 can include a cloud gateway component 1408 that canapply appropriate pre-processing to the gathered industrial data 1414prior to pushing (e.g., communicating) the data to the cloud-basedanalytics system or modeler system as cloud data 1404. Firewall box 1412can allow industrial devices 1406 ₁, 1406 ₂, and/or (up through) 1406_(N) to interact with the cloud platform without directly exposing theindustrial devices to the Internet.

In some embodiments, the cloud gateway 1308 of FIG. 13 or cloud gateway1408 of FIG. 14 can tag the collected industrial data (e.g., 1314 or1414) with contextual metadata prior to pushing the data as cloud data(e.g., 1304 or 1404) to the cloud platform. Such contextual metadata caninclude, for example, a time stamp, a location of the device at the timethe data was generated, or other contextual information. In anotherexample, some cloud-aware devices can comprise smart devices capable ofdetermining their own context within the plant or enterpriseenvironment. Such devices can determine their location within ahierarchical plant context or device topology. Data generated by suchdevices can adhere to a hierarchical plant model that can definemultiple hierarchical levels of an industrial enterprise (e.g., aworkcell level, a line level, an area level, a site level, an enterpriselevel, etc.), such that the data can be identified (e.g., by theanalytics system or modeler system) in terms of these hierarchicallevels. This can allow a common terminology to be used across an entireindustrial enterprise to identify devices and their associated data.Cloud-based applications and services that model an enterprise accordingto such an organizational hierarchy can represent industrialcontrollers, devices, machines, or processes as data structures (e.g.,type instances) within this organizational hierarchy to provide contextfor data generated by respective devices within the enterprise relativeto the enterprise as a whole. Such a convention can replace the flatname structure that is employed by some industrial applications.

In some embodiments, the cloud gateway 1308 of FIG. 13 or cloud gatewaycomponent 1408 of FIG. 14 can comprise uni-directional “data only”gateways that can be configured only to move data from the premises(e.g., industrial facility) to the cloud platform. Alternatively, thecloud gateway components 1308 and 1408 can comprise bi-directional “dataand configuration” gateways that additionally can be configured toreceive configuration or instruction data from services running on thecloud platform. Some cloud gateways can utilize store-and-forwardtechnology that can allow the gathered industrial data (e.g., 1314 or1414) to be temporarily stored locally on storage associated with thecloud gateway component (e.g., 1308 or 1408) in the event thatcommunication between a gateway and the cloud platform is disrupted. Insuch events, the cloud gateway component (e.g., 1308 or 1408) canforward (e.g., communicate) the stored data to the cloud platform whenthe communication link is re-established.

To ensure a rich and descriptive set of data for analysis purposes, thecloud-based analytics system or modeler system can collect device datain accordance with one or more standardized device models. To this end,a standardized device model can be developed for each industrial device.Device models can profile the device data that is available to becollected and maintained by the analytics system or modeler system.

FIG. 15 illustrates a block diagram of an example device model 1500according to various aspects and implementations of the disclosedsubject matter. In the illustrated example model 1500, the device model1506 can be associated with a cloud-aware industrial device 1502 (e.g.,a programmable logic controller, a variable frequency drive, an HMI, avision camera, a barcode marking system, etc.). As a cloud-aware device,the industrial device 1502 can be configured to automatically detect andcommunicate with the cloud platform 1508 upon installation at a plantfacility, simplifying integration with existing cloud-based datastorage, analysis, and applications (e.g., as performed by the analyticssystems, modeler systems, and/or virtualization systems describedherein). When added to an existing industrial automation system, theindustrial device 1502 can communicate with the cloud platform and cansend identification and configuration information in the form of thedevice model 1506 to the cloud platform 1508. The device model 1506 canbe received by the modeler system 1510 (or analytics system (not shownin FIG. 15)), which can update the customer's device data 1514 based onthe device model 1506. In this way, the modeler system 1510 (oranalytics system) can leverage the device model 1506 to facilitateintegrating the new industrial device 1502 into the greater system as awhole. This integration can include the modeler system 1510 (oranalytics system) updating cloud-based applications or services torecognize the new industrial device 1502, determining one or morecorrelations between the new industrial device 1502 and other aspects(e.g., industrial assets, extrinsic events or conditions) associatedwith the industrial automation system, adding the new industrial device1502 to a dynamically updated data model of the customer's industrialenterprise or plant, modifying a model to integrate, incorporate, orinclude a model of the new industrial device 1502 based at least in parton the identification and configuration information (or other data), ormodifying a virtualization industrial automation system associated withthe industrial automation system to integrate, incorporate, or include avirtualized version of the new industrial device 1502 based at least inpart on the identification and configuration information (or otherdata), determining or predicting a response of the modified industrialautomation system based at least in part on a modified model or modifiedsimulation model that integrates the new industrial device 1502, makingother devices on the plant floor aware of the new industrial device1502, or other desired integration functions. Once deployed, some dataitems comprising the device model 1506 can be collected and monitored bythe modeler system 1510 (or analytics system) on a real-time or nearreal-time basis.

The device model 1506 can comprise such information as a deviceidentifier (e.g., model and serial number) associated with theindustrial device 1502, status information for the industrial device1502, a currently installed firmware version associated with theindustrial device 1502, device setup data associated with the industrialdevice 1502, warranty specifications associated with the industrialdevice 1502, calculated and/or anticipated KPIs associated with theindustrial device 1502 (e.g., mean time between failures), health anddiagnostic information associated with the industrial device 1502,device documentation, or other such parameters.

In addition to maintaining individual customer-specific data stores foreach industrial enterprise, the modeler system (e.g., cloud-basedmodeler system), or analytics system (e.g., cloud-based analyticssystem), also can feed (e.g., transmit) sets of customer data to aglobal data storage (referred to herein as cloud-based data store or BigData for Manufacturing (BDFM) data store) for collective big dataanalysis in the cloud platform (e.g., by the virtualization system).FIG. 16 presents a block diagram of an example system 1600 that canfacilitate collection of data from devices and assets associated withrespective industrial automation systems for storage in cloud-based datastorage, in accordance with various aspects and implementations of thedisclosed subject matter. As illustrated in FIG. 16, the collectioncomponent 1010 of the modeler system (e.g., as facilitated by theinterface component 1012) can collect data from devices and assetscomprising respective different industrial automation systems, such asindustrial automation system₁ 1606 ₁, industrial automation system₂ 1606₂, and/or (up through) industrial automation system_(N) 1606 _(N), forstorage in a cloud-based BDFM data store 1602. In some embodiments, datamaintained in the BDFM data store 1602 can be collected anonymously withthe consent of the respective customers. For example, customers canenter into a service agreement with a technical support entity wherebythe customer can agree to have their device and asset data collected bythe analytics system, modeler system, and/or virtualization system inexchange for analytics-related services, modeling-related services,and/or virtualization-related services or a credit towardsanalytics-related services, modeling-related services, and/orvirtualization-related services. The data maintained in the BDFM datastore 1602 can include all or portions of the classifiedcustomer-specific data described in connection with FIG. 11, as well asadditional data (e.g., derived, determined, or inferred data). Theanalytics component 1000 (e.g., aggregator component 1004, analyticsmanagement component 1016, etc.) or another component of the analyticssystem can organize the collected data stored in the BDFM data store1602 according to device type, system type, application type, applicableindustry, or other relevant categories. The analytics managementcomponent 1016 can analyze data stored in the resulting multi-industry,multi-customer data store (e.g., BDFM data store 1602) to facilitatelearning, determining, or identifying industry-specific,device-specific, and/or application-specific trends, patterns,thresholds (e.g., device-related thresholds, network-related thresholds,etc.), industrial-automation-system interrelationships between devicesor assets, etc., associated with the industrial automation systemsassociated with the cloud platform. In general, the analytics managementcomponent 1016 can perform a data analysis (e.g., big data analysis) ondata (e.g., the multi-industrial enterprise data) maintained (e.g.,stored in) the BDFM data store 1602 to facilitate learning, determining,identifying, characterizing, virtualizing, simulating, and/or emulatingoperational industrial-automation-system interrelationships,correlations, thresholds, trends, or patterns associated with industrialautomation systems as a function of industry type, application type,equipment in use, asset configurations, device configuration settings,or other types of variables.

For example, it can be known that a given industrial asset (e.g., adevice, a configuration of device, a machine, etc.) can be used acrossdifferent industries for different types of industrial applications.Accordingly, the analytics management component 1016 can identify asubset of the global data stored in BDFM data store 1602 relating to theasset or asset type, and perform analysis on this subset of data todetermine how the asset or asset type performs over time and undervarious types of operating conditions for each of multiple differentindustries or types of industrial applications. The analytics managementcomponent 1016 also can determine the operational behavior of the assetor asset type over time and under various types of operating conditionsfor each of different sets of operating constraints or parameters (e.g.different ranges of operating temperatures or pressures, differentrecipe ingredients or ingredient types, etc.). The analytics managementcomponent 1016 can leverage (e.g., use) a large amount of historicaldata relating to the asset or asset type that has been gathered (e.g.,collected and/or aggregated) from many different industrial automationsystems to facilitate learning or determining common operatingcharacteristics of many diverse configurations of industrial assets orasset types at a relatively high degree of granularity and under manydifferent operating contexts. The analytics management component 1016can use the learned or determined operating characteristics relating tothe industrial assets or asset types to facilitate determiningcorrelations between respective items of interest associated with anindustrial automation system(s), visualizing information relating to theindustrial automation system(s) for a user, determining changes tooperations or industrial assets associated with the industrialautomation system(s) that can facilitate improving operations associatedwith the industrial automation system(s) and/or achieving desired goalswith respect to the industrial automation system(s), and/or determiningand providing notifications, recommendations, or instructions relatingto the correlations between the respective items of interest or thedetermined changes to operations or industrial assets associated withthe industrial automation system. The modeler component and/orvirtualization component can use the learned or determined operatingcharacteristics relating to the industrial assets or asset types tofacilitate generating, updating, and/or using modeled versions orvirtualized versions of the industrial assets or asset types whenemployed in an industrial automation system to facilitate generating,updating, and/or using a model of an industrial automation component ora virtualized industrial automation system that can be based at least inpart on the modeled or virtualized versions of the industrial assets orasset types.

FIG. 17 illustrates a block diagram of a cloud-based system 1700 thatcan employ a analytics system and modeler system to facilitateperforming or providing analytics-related services and modeler-relatedservices associated with industrial automation systems, in accordancewith various aspects and embodiments of the disclosed subject matter. Asdisclosed herein, the analytics system 1702 and modeler system 1704 cancollect, maintain, and monitor customer-specific data (e.g. device data1110, process data 1112, asset data 1114, and system data 1116) relatingto one or more industrial assets 1706 of an industrial enterprise. Inaddition, the analytics system 1702 and modeler system 1704 can collectand organize industrial data anonymously (with customer consent) frommultiple industrial enterprises, and can store such industrial data in aBDFM data store 1708 for collective analysis by the analytics system1702 and/or modeler system 1704, for example, as described herein.

The analytics system 1702 and modeler system 1704 also can collectproduct resource information and maintain (e.g., store) the productresource information in the cloud-based product resource data store1710. In general, the product resource data store 1710 can maintainup-to-date information relating to specific industrial devices or othervendor products in connection with industrial automation systems.Product data stored in the product resource data store 1710 can beadministered by the analytics system 1702 and/or modeler system 1704and/or one or more product vendors or OEMs. Exemplary device-specificdata maintained by the product resource data store 1710 can includeproduct serial numbers, most recent firmware revisions, preferred deviceconfiguration settings and/or software for a given type of industrialapplication, or other such vendor-provided information.

The system depicted in FIG. 17 can provide analytics-related servicesand model-related services to subscribing customers (e.g., owners ofindustrial assets 1706). For example, customers can enter an agreementwith a product vendor or technical support entity to allow their systemdata to be gathered anonymously and fed into (e.g., communicated to andstored in) the BDFM data store 1708, and this thereby can expand thestore of global data available for collective analysis by the analyticssystem 1702 and/or the modeler system 1704. In exchange, the vendor ortechnical support entity can agree to provide analytics-related servicesand/or model-related services (e.g., customized model-related services)to the customer (e.g., real-time or near real-time system monitoring;real-time or near real-time performance of analytics on data anddetermination of correlations relating to an industrial automationsystem; real-time or near real-time generation, updating, and/or use ofa model or a virtualized industrial automation system associated with anindustrial automation system, etc.). Alternatively, the customer cansubscribe to one or more available analytics-related services ormodel-related services that can be provided by the analytics system 1702or modeler system 1704, and optionally can allow their system data to bemaintained in the BDFM data store 1708. In some embodiments, a customercan be given an option to subscribe to analytics-related services ormodel-related services without permitting their data to be stored in theBDFM data store 1708 for collective analysis with data from othersystems (e.g., industrial automation systems). In such cases, thecustomer's data will only be maintained as customer data (e.g., incustomer data store 1108) for the purposes of real-time or near-realtime performance of analytics on data, visualization of informationrelating to an industrial automation system, and/or determination ofrecommendations or instructions to facilitate improving operationsassociated with the industrial automation system, relating to thatparticular customer, and/or real-time or near real-time generation,updating, and/or use of a model or a virtualized industrial automationsystem associated with an industrial automation system relating to thatparticular customer, and the collected customer data will be analyzed inconnection with data stored in the BDFM data store 1708 and the productresource data store 1710 without that customer data being migrated forstorage in the BDFM data store 1708 for long-term storage and analysis.In another exemplary agreement, customers can be offered a discount onanalytics-related services or model-related services in exchange forallowing their system data to be anonymously migrated to the BDFM datastore 1708 for collective analysis by the analytics system 1702 ormodeler system 1704.

In accordance with various aspects, the customer-specific data caninclude device and/or asset level faults and alarms, process variablevalues (e.g., temperatures, pressures, product counts, cycle times,etc.), calculated or anticipated key performance indicators for thecustomer's various assets, indicators of system behavior over time, andother such information. The customer-specific data also can includedocumentation of firmware versions, configuration settings, and softwarein use on respective devices of the customer's industrial assets.Moreover, the analytics system 1702 or modeler system 1704 can take intoconsideration customer information encoded in customer model 1118, whichcan have a bearing on inferences made by the analytics system 1702 ormodeler system 1704 based at least in part on the analysis (e.g., bigdata analysis) stored in the BDFM data store 1708. For example, customermodel 1118 may indicate a type of industry that is the focus of thecustomer's business (e.g., automotive, food and drug, oil and gas,fibers and textiles, power generation, marine, etc.). Knowledge of thecustomer's industry can facilitate enabling the analytics system 1702 ormodeler system 1704 to correlate the customer-specific data with datarelating to similar systems and applications in the same industry, asdocumented by the data stored in the BDFM data store 1708.

Taken together, customer-specific data and a customer model (e.g., 1118)can facilitate accurately modeling the customer's industrial enterpriseat a highly granular level, from high-level system behavior over timedown to the device and software level. The analyzing (e.g., by theanalytics system 1702 or modeler system 1704) of this customer-specificdata in view of global industry-specific and application-specific trendslearned via analysis of data stored in the BDFM data store 1708, as wellas vendor-provided device information maintained in the product resourcedata store 1710, can facilitate real-time or near-real time performanceof analytics on data, visualization of information relating to anindustrial automation system (e.g., customized visualization ofinformation based on correlations between respective items of interestassociated with the industrial automation system), and/or determinationof recommendations or instructions to facilitate improving operationsassociated with the industrial automation system, and can facilitatereal-time or near real-time generation, updating, and/or use of a modelor a virtualized industrial automation system associated with anindustrial automation system to facilitate real-time or near real-timeremote interaction with (e.g., monitoring, tracking, controlling, etc.,of) the industrial automation system using the model or the virtualizedindustrial automation system (e.g., based at least in part on userinteractions with the virtualized industrial automation system by a uservia a communication device).

In some implementations, the system 1700 (e.g., via the collectioncomponent, analytics system 1702, or modeler system 1704) also canreceive, collect, or capture extrinsic data 1712 from one or moresources (e.g., external data sources). The analytics system 1702 ormodeler system 1704 can use or leverage the extrinsic data 1712received, collected, or captured from sources external to a customer'sindustrial enterprise, wherein the extrinsic data 1712 can haverelevance to operation of the customer's industrial automationsystem(s). Example extrinsic data 1712 can include, for example, energycost data, material cost and availability data, transportation scheduleinformation from companies that provide product transportation servicesfor the customer, market indicator data, web site traffic statistics,information relating to known information security breaches or threats,or other information relevant to the operation of the customer'sindustrial automation system(s). The analytics system 1702 or modelersystem 1704 can retrieve extrinsic data 1712 from substantially any datasource, such as, e.g., servers or other data storage devices linked tothe Internet, cloud-based storage that maintains extrinsic data ofinterest, or other sources. The analytics system 1702 or modeler system1704 can analyze the extrinsic data 1712 and/or other data (e.g.,user-related data associated with users (e.g., operators, managers,technicians, other workers) associated with the industrial automationsystem(s), device data 1110, process data 1112, asset data 1114, systemdata 1116, etc.) to facilitate performing analytics-related services,visualization-related services, modeling-related services,virtualization-related services, or other services in connection withthe industrial automation system(s).

The aforementioned systems and/or devices have been described withrespect to interaction between several components. It should beappreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentsmay be combined into a single component providing aggregatefunctionality. The components may also interact with one or more othercomponents not specifically described herein for the sake of brevity,but known by those of skill in the art.

FIGS. 18-22 illustrate various methods in accordance with one or moreembodiments of the subject application. While, for purposes ofsimplicity of explanation, the one or more methods shown herein areshown and described as a series of acts, it is to be understood andappreciated that the disclosed subject matter is not limited by theorder of acts, as some acts may, in accordance therewith, occur in adifferent order and/or concurrently with other acts from that shown anddescribed herein. For example, those skilled in the art will understandand appreciate that a method could alternatively be represented as aseries of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement a methodin accordance with the disclosed subject matter. Furthermore,interaction diagram(s) may represent methods, in accordance with thesubject disclosure when disparate entities enact disparate portions ofthe methods. Further yet, two or more of the disclosed example methodscan be implemented in combination with each other, to accomplish one ormore features or advantages described herein.

FIG. 18 illustrates a flow diagram of an example method 1800 that canperform analytics on industrial-automation-system-related data (e.g.,cloud-based data) to determine correlations between respective items ofinterest associated with an industrial automation system associated withan industrial enterprise, in accordance with various implementations andembodiments of the disclosed subject matter. The method 1800 can beimplemented by an analytics system that can comprise an analyticscomponent that can comprise or be associated with a collectioncomponent, a data store, and/or an analytics management component, etc.All or a desired portion of the analytics system can reside in a cloudplatform.

At 1802, a set of industrial-automation-system-related data associatedwith an industrial automation system can be collected. The collectioncomponent can obtain, collect, or otherwise receiveindustrial-automation-system-related data and can store such data in acloud-based data store. The collection component also can receive otherdata, including other industrial-automation-system-related data fromanother (e.g., a related) industrial automation system or one or moreextrinsic data sources.

The set of industrial-automation-system-related data can comprise, forexample, device-related data (e.g., industrial device-related data,network device-related data), asset-related data, process-related data(e.g., industrial-automation-process-related data), data relating tousers associated with the industrial automation system (e.g., roleinformation, user preferences, etc.), and/or otherindustrial-automation-system-related data associated with an industrialenterprise. The industrial-automation-system-related data can bemigrated (e.g., communicated) to the cloud platform using one or morecloud gateways (e.g., communication gateway components) that can serveas uni-directional or bi-directional communication interfaces betweenindustrial devices or assets of the industrial automation system and thecloud platform. The device-related data, asset-related data,process-related data, and/or other industrial-automation-system-relateddata can be stored in the cloud-based data store in association withidentification information, such as, for example, a customer identifierand other customer-specific information.

At 1804, analytics can be performed on the set ofindustrial-automation-system-related data to determine one or morecorrelations between respective items of interest relating to theindustrial automation system, based at least in part on the results ofan analysis of the set of industrial-automation-system-related data, tofacilitate improving (e.g., optimizing, making more acceptable)performance of the operation of the industrial automation system orassociated users (e.g., employees). The analytics management componentcan access the cloud-based data store and can receive (e.g., collect,obtain, etc.) the set of industrial-automation-system-related data fromthe cloud-based data store. The analytics management component cananalyze the set of industrial-automation-system-related data to generateanalysis results. The analytics management component can determine oridentify one or more correlations between respective items of interestrelating to the industrial automation system, based at least in part onthe analysis results. The respective items of interest can relate tointernal factors (factors within the industrial facility comprising theindustrial automation system), such as, for example, an industrialdevice, industrial process, other type of industrial asset,network-related device, device or process parameters, material inventorywithin the industrial facility, production output, employee behavior orinteraction with the industrial automation system, asset maintenance,repair, or replacement, or asset downtime; or external (e.g., extrinsic)factors, such as, for example, material inventory of a supplier, weatherconditions (e.g., weather conditions in the area of the industrialfacility, weather conditions that can affect consumer demand for aproduct, weather conditions that can affect the supplying of materialsfor the product to the industrial facility), demand for a product byconsumers, energy costs associated with producing a product,transportation costs relating to transporting product or product-relatedmaterials, or governmental policies or laws.

FIG. 19 depicts a flow diagram of another example method 1900 that canperform analytics on industrial-automation-system-related data (e.g.,cloud-based data) to determine correlations between respective items ofinterest associated with a set of industrial automation systemsassociated with an industrial enterprise and generate recommendations,notifications, or instructions relating to the correlations tofacilitate improving operation of the set of industrial automationsystems, in accordance with various implementations and embodiments ofthe disclosed subject matter. The method 1900 can be implemented by ananalytics system that can comprise an analytics component that cancomprise or be associated with a collection component, a data store,and/or an analytics management component, etc. All or a desired portionof the analytics system can reside in a cloud platform.

At 1902, a set of data (e.g., industrial-automation-system-related data)relating to a set of industrial automation systems comprising one ormore industrial automation systems can be collected. The analyticscomponent can monitor and track operations of the industrial automationsystems of the set of industrial automation systems, employeeinteractions with the industrial automation systems, and/or extrinsicfactors (e.g., weather conditions, supplying of materials for products,product demand, transportation costs associated with products, energycosts) with respect to the set of industrial automation systems, etc.Based at least in part on the monitoring and tracking, data (e.g.,industrial-automation-system-related data) can be obtained by, migrated,or otherwise received by the cloud platform. The collection componentcan collect the set of data relating to the set of industrial automationsystems. The set of data can comprise data relating to industrialdevices, industrial processes, other industrial assets, and/ornetwork-related devices, etc., associated with the one or moreindustrial automation systems of the set of industrial automationsystems. The set of industrial automation systems can be associated withone or more industrial enterprises.

Respective subsets of the data can be obtained from respectiveindustrial devices, industrial processes, other industrial assets,and/or network-related devices via one or more cloud gateway devices(e.g., respective cloud gateways integrated with the respective devices,processes, assets, etc.). For instance, the analytics component ormodeler component can discover the respective industrial devices,industrial processes, other industrial assets, and/or network-relateddevices in the industrial automation system, and the respectiveindustrial devices, industrial processes, other industrial assets,and/or network-related devices can provide their respective subsets ofdata to the analytics component or modeler component via the one or morecloud gateway devices, in response to being polled (e.g., queried) bythe analytics component or modeler component.

At 1904, the set of data can be stored in a data store. The collectioncomponent can facilitate storing the set of data in the data store,wherein the data store can be a cloud-based data store located in thecloud platform.

At 1906, the set of data can be analyzed. The analytics managementcomponent can access the cloud-based data store and can retrieve,obtain, read the set of data from the cloud-based data store. Theanalytics management component can analyze the set of data (e.g.,perform big data analysis on the set of data) to facilitate determiningone or more correlations between respective items of interest associatedwith the set of industrial automation systems, wherein the respectiveitems of interest can relate to internal factors with respect to the setof industrial automation systems or extrinsic factors with respect tothe set of industrial automation systems.

At 1908, one or more correlations can be determined between respectiveitems of interest associated with the set of industrial automationsystems based at least in part on the results of the data analysis. Theanalytics management component can determine one or more correlationsbetween respective items of interest associated with the set ofindustrial automation systems based at least in part on the dataanalysis results. For example, based at least in part on the dataanalysis results, the analytics management component can determine thatthere is a correlation between a first item of interest, e.g., aparameter setting (e.g., a speed parameter value for the motor)) for anindustrial device (e.g., motor) of an industrial automation system, anda second item of interest, e.g., product breakage and/or misalignment ofproducts on a conveyor. The respective items of interest, and thedetermined correlations, also can span across multiple industrialautomation systems and/or can relate to extrinsic factors that can beexternal to an industrial automation system.

At 1910, one or more recommendations, instructions, and/or notificationscan be determined based at least in part on the one or morecorrelations. Based at least in part on the one or more correlations,the analytics management component can determine one or more changesthat can be made in connection with one or more industrial automationsystems of the set of industrial automation systems to facilitateimproving operation of the set of industrial automation systems. Theanalytics management component can determine and generate one or morerecommendations, instructions, and/or notifications relating to the oneor more changes that can be made in connection with one or moreindustrial automation systems of to facilitate improving operation ofthe set of industrial automation systems. For example, based at least inpart on the determined correlation between the first item of interestand the second item of interest, the analytics management component candetermine a change (e.g., adjustment, modification) that can be made tothe parameter setting of the industrial device (e.g., to slow the motordown) to facilitate reducing or minimizing product breakage and/ormisalignment of products on the conveyor.

At 1912, one or more recommendations, instructions, and/or notificationsrelating to one or more changes that can be made in connection with oneor more industrial automation systems, to facilitate improvingperformance associated with the one or more industrial automationsystem, can be presented (e.g., communicated, displayed). The analyticscomponent can present the one or more recommendations, instructions,and/or notifications, which can relate to the one or more changes thatcan be made in connection with one or more industrial automationsystems, to a communication device associated with a user or anindustrial automation system(s) for consideration or action by the useror the industrial automation system(s). For example, the analyticscomponent can present a notification message to the user (e.g., via thecommunication device of the user) to facilitate notifying the user ofthe correlation between the first item of interest and second item ofinterest and/or the negative impact on system performance (e.g., productbreakage and/or misalignment of products on the conveyor) relating tothe correlation, a recommendation message to the user (e.g., via thecommunication device of the user), wherein the recommendation recommendsthat a change (e.g., adjustment, modification) be made to the parametersetting of the industrial device to facilitate reducing or minimizingproduct breakage and/or misalignment of products on the conveyor, orinstructions to the motor or a controller associated with the motor tofacilitate changing the parameter setting associated with the motorbased on the instructions to facilitate reducing or minimizing productbreakage and/or misalignment of products on the conveyor.

FIG. 20 presents a flow diagram of an example method 2000 that can rankand prioritize respective correlations between respective items ofinterest associated with an industrial automation system to facilitateimproving operation of the industrial automation system, in accordancewith various implementations and embodiments of the disclosed subjectmatter. The method 2000 can be implemented by an analytics system thatcan comprise an analytics component that can comprise or be associatedwith a collection component, a data store, and/or an analyticsmanagement component, etc. All or a desired portion of the analyticssystem can reside in a cloud platform.

At 2002, a set of correlations, comprising respective correlationsrelating to respective items of interest, associated with the industrialautomation system can be determined based at least in part on theresults of analytics performed on a set of data relating to theindustrial automation system. The analytics management component candetermine the set of correlations based at least in part on the resultsof the analytics performed on the set of data relating to the industrialautomation system.

At 2004, the relative priority of the respective correlations of the setof correlations can be determined based at least in part on theanalytics results (e.g., results of the analytics relating to therespective correlations), in accordance with the defined analyticscriteria. In accordance with the defined analytics criteria, there canbe one or more factors that can be considered and applied in determiningthe relative priority of the respective correlations. For example, inaccordance with the defined analytics criteria, the one or more factorscan relate to the relative impact or importance on respective definedgoals (e.g., production output, revenue from products, profits fromproducts, minimization of waste or breakage associated with production)of the respective correlations, wherein the respective correlations canaffect respective defined goals differently (e.g., one correlationaffecting a certain goal more than other correlations), and whereincertain goals can be considered more important than other goals. In someimplementations, the analytics management component can respectivelyweight the respective defined goals, in accordance with the definedanalytics criteria, to facilitate reflecting (e.g., taking into account)the relative impact or importance of the respective defined goals withrespect to each other (e.g., as defined by an entity associated with theindustrial automation system). The analytics management component cananalyze the respective correlations, and data respectively associatedtherewith, and can determine the relative priority of the respectivecorrelations of the set of correlations based at least in part on theanalysis results, the defined analytics criteria, and/or the respectiveweightings of the respective defined goals.

At 2006, the respective correlations can be ranked with respect to eachother in accordance with their respective order of importance or impactbased at least in part on the respectively determined priorities of therespective correlations. The analytics management component can rank therespective correlations with respect to each other in accordance withtheir respective order of importance or impact based at least in part onthe respectively determined priorities of the respective correlations,wherein a correlation having a higher importance or impact can be rankedhigher in priority than a correlation having a lower importance orimpact.

At 2008, one or more recommendations, instructions, or notificationsrelating to operation of the industrial automation system can bedetermined based at least in part on the respective priority rankings ofthe respective correlations. The analytics management component candetermine one or more recommendations, instructions, or notificationsrelating to operation of the industrial automation system based at leastin part on the respective priority rankings of the respectivecorrelations. For instance, the analytics management component cananalyze the respective priority rankings of the respective correlationsand can determine that a first recommendation or instruction is to bemade to facilitate a first change to an aspect (e.g., configuration orparameter setting of an industrial asset) of the industrial automationsystem based at least in part on a first correlation relating to theaspect, wherein the first correlation is ranked first in priorityrelative to the other correlations. This can be due to the recommendedor instructed change being determined (e.g., by the analytics managementcomponent) to have the most desired (e.g., the most positive orfavorable) impact on the operation of the industrial automation systemin connection with a particular defined goal or set of defined goals.

In some instances, acting on a highest-ranked correlation (e.g., makinga change associated with respect to one aspect associated with anindustrial automation system related to the highest-ranked correlation)can have an undesirable (e.g., negative) impact with respect to anotheraspect associated with the industrial automation system that isassociated with a lower-ranked correlation. However, in accordance withthe defined analytics criteria, due in part to the determined benefitand/or importance of making the change with respect to the one aspectassociated with an industrial automation system related to thehighest-ranked correlation, the analytics management component candetermine that the change with respect to the one aspect is to be madeor recommended to be made, even though there may be some undesirableimpact with respect to the other aspect associated with the industrialautomation system that is associated with the lower-ranked correlation.

The respective priority rankings of the respective correlationsassociated with the industrial automation system also can facilitateenabling a user to identify the relative importance of making therespective recommended changes to the respective aspects of theindustrial automation system associated with the respective priorityrankings of the respective correlations. In this way, a user can knowwhich correlations and recommended changes are more important thanothers, and the user can determine what changes in connection with anindustrial automation system are to be made, and can prioritize theorder of the making of changes in connection with an industrialautomation system, based at least in part on the recommendations, whichcan be correspondingly prioritized (e.g., by the analytics managementcomponent), in accordance with the respective priority rankings of therespective correlations associated with the industrial automationsystem.

At 2010, all or a portion of the one or more recommendations,instructions, or notifications relating to operation of the industrialautomation system can be presented (e.g., to a user or the industrialautomation system), based at least in part on the respective priorityrankings of the respective correlations, in accordance with the definedanalytics criteria. The analytics management component can present(e.g., to user via a communication device, to the industrial automationsystem) all or a portion of the one or more recommendations,instructions, or notifications relating to the operation of theindustrial automation system, based at least in part on the respectivepriority rankings of the respective correlations, in accordance with thedefined analytics criteria, to facilitate improved operation of theindustrial automation system (e.g., based on the consideration or actionof the user or industrial automation system in response to all or aportion of the one or more recommendations, instructions, ornotifications relating to the operation of the industrial automationsystem).

FIG. 21 presents a flow diagram of an example method 2100 that candetermine baselines for respective variables associated with anindustrial automation system and determine a deviation from a baselineof a variable associated with an industrial automation system, inaccordance with various implementations and embodiments of the disclosedsubject matter. The method 2100 can be implemented by an analyticssystem that can comprise an analytics component that can comprise or beassociated with a collection component, a data store, and/or ananalytics management component, etc. All or a desired portion of theanalytics system can reside in a cloud platform.

At 2102, analytics can be performed on a set of data relating to anindustrial automation system. The analytics management component canaccess the cloud-based data store and can retrieve, obtain, read the setof data from the cloud-based data store, wherein the data can becollected from or in connection with the industrial automation system.The analytics management component can perform analytics on the set ofdata (e.g., perform big data analysis on the set of data) to facilitatedetermining respective baselines of a set of baselines for respectivevariables associated with the industrial automation system. The set ofvariables can relate to, for example, production output of a product bythe industrial automation system, a cost associated with producing theproduct, an amount of material used to produce a product, an amount oftime to produce the product, or a performance of an employee inperforming a work task in connection with the industrial automationsystem and/or producing the product.

At 2104, respective baselines (e.g., baseline values, levels, and/orresults) for respective variables associated with the industrialautomation system can be determined based at least in part on theresults of the data analytics. The analytics management component candetermine the respective baselines (e.g., performance baselines orguidelines) for the respective variables associated with the industrialautomation system based at least in part on the data analytics results.

At 2106, operation of the industrial automation system and employeeinteraction with respect to the industrial automation system can bemonitored and tracked. The analytics management component can monitorand track the operation of the industrial automation system and employeeinteraction (e.g., work performance or behavior) with respect to theindustrial automation system to facilitate determining whether avariable associated with the industrial automation system is deviatingor potentially can deviate (e.g., is projected to deviate based on atrend, or is in danger of or substantially close to deviating) from abaseline associated with the variable.

At 2108, a deviation or potential for deviation from a baseline of avariable associated with the industrial automation system can bedetermined based at least in part on results of an analytics performedon data relating to the operation of the industrial automation systemand employee interaction with respect to the industrial automationsystem. The analytics management component can determine the deviationor potential for deviation from the baseline of the variable associatedwith the industrial automation system based at least in part on the dataanalytics results. For example, the analytics management component cananalyze the data and, based at least in part on the analytics results,can determine that a variable (e.g., production output, a variablerelating to performance of a work task by an employee, an amount ofwaste in connection with producing a product, a cost associated withproducing a product) is deviating or is predicted to deviate from therelated (e.g., applicable) baseline for that variable.

At 2110, one or more recommendations, instructions, and/or notificationsrelating to one or more changes that can be made in connection with theindustrial automation system, to facilitate avoiding or at leastmitigating the deviation or potential for deviation from the baselineassociated with the variable, can be presented (e.g., communicated,displayed). The analytics component can determine and present the one ormore recommendations, instructions, and/or notifications, which canrelate to the one or more changes that can be made in connection withthe industrial automation system, to a communication device associatedwith a user or an industrial automation system(s) for consideration oraction by the user or the industrial automation system(s). For example,the analytics component can present a notification message to the user(e.g., via the communication device of the user) to facilitate notifyinga user of the deviation or potential for deviation of a variable from abaseline associated with the industrial automation system, arecommendation message to the user (e.g., via the communication deviceof the user), wherein the recommendation message can recommend that achange (e.g., adjustment, modification) be made in connection with thevariable (e.g., asset-related variable and/or employee-related variableand/or other variable relating to the industrial automation system) tofacilitate alleviating, preventing, or mitigating a deviation of thevariable from the baseline, or instructions to the an industrialasset(s) associated with the industrial automation system, wherein theinstructions can facilitate changing (e.g., adjusting, modifying) anaspect (e.g., parameter setting, configuration) associated with theindustrial asset(s) to facilitate alleviating, preventing, or mitigatingthe deviation of the variable from the baseline.

FIG. 22 depicts a flow diagram of an example method 2200 that cancustomize information displays relating to correlations betweenrespective items of interest that are associated with an industrialautomation system, in accordance with various implementations andembodiments of the disclosed subject matter. The method 2200 can beimplemented by an analytics system that can comprise an analyticscomponent that can comprise or be associated with a collectioncomponent, a data store, visualization component, and/or an analyticsmanagement component, etc. All or a desired portion of the analyticssystem can reside in a cloud platform.

At 2202, an information display that can provide information relating toan industrial automation system can be presented. The analyticsmanagement component can generate information relating to an industrialautomation system, or a portion thereof, based at least in part onresults of an analysis of industrial-automation-system-related datacollected from or in connection with the industrial automation system.The visualization component can generate an information display that canprovide the information relating to the industrial automation system,and can present the information display to a user, for example, via acommunication device of the user. The information display generated bythe visualization component can be a multi-dimensional (e.g., 2-D or3-D) visualization, and can be presented in one or more formats, suchas, for example, a multi-dimensional virtualization or modeling of allor a portion of the industrial automation system, a customized dataoverlay that can be overlaid on respective portions (e.g., respectivevirtualized or modeled industrial assets) of the virtualized or modeledindustrial automation system, a chart, a graph, a list of data values.In some implementations, the formats of visualization of the informationdisplay generated by the visualization component can be determinedand/or or customized by the visualization component based at least inpart on an identifier, a role, an authentication credential, a userpreference, or access rights to the industrial-automation-system-relatedinformation associated with the user, and/or other factors, inaccordance with the defined analytics criteria.

At 2204, input information relating to at least two items of interestrelating to the industrial automation system can be received tofacilitate selection of the at least two items of interest. The user canenter input information (e.g., selection information) into a userinterface of the user's communication device or another user interfaceto facilitate selecting the at least two items of interest to the user,wherein such input information can be communicated from thecommunication device or another device associated with the other userinterface to the analytics component. The analytics component canreceive the input information. Respective items of interest of the atleast two items of interest can relate to respective portions (e.g.,industrial asset(s)) or aspects (e.g., product output, material suppliesfor product available at the industrial facility, production goal(s)) ofthe industrial automation system or respective extrinsic conditions(e.g., weather conditions, transportation costs), events (e.g., changein material supplies provided by a supplier, train derailment thataffects the supply of materials), or factors associated with theindustrial automation system, such as more fully disclosed herein.

At 2206, the at least two items of interest relating to the industrialautomation system can be determined based at least in part on theresults of an analysis of the input information. The analyticsmanagement component or visualization component can analyze the inputinformation, and can determine or identify the at least two items ofinterest selected by the user.

At 2208, one or more correlations between respective items of interestof the at least two items of interest can be determined based at leastin part on analytics performed on the data relating to the industrialautomation system. The analytics management component can performanalytics on the data relating to the industrial automation system todetermine correlations between respective items of interest relating tothe industrial automation system. As part of performing the analytics,the analytics management component can perform analytics on the data todetermine correlations between the at least two items of interest thatwere selected by the user.

At 2210, a customized information display can be determined based atleast in part on one or more correlations between the respective itemsof interest of the at least two items of interest. The visualizationcomponent can determine the customized information display based atleast in part on one or more correlations between the respective itemsof interest selected by the user, the identifier, role, authenticationcredential, user preference, or access rights to theindustrial-automation-system-related information associated with theuser, and/or other factors, in accordance with the defined analyticscriteria. The customized information display (e.g., 2-D or 3-Dcustomized information display) can present information relating to theone or more correlations between the respective items of interestselected by the user in one or more desired formats of visualization,wherein the visualization component can determine the one or moredesired formats of visualization to employ when generating thecustomized information display based at least in part on the respectiveitems of interest selected by the user, the identifier, role,authentication credential, user preference, or access rights to theindustrial-automation-system-related information associated with theuser, and/or other factors, in accordance with the defined analyticscriteria. The information relating to the one or more correlations cancomprise information describing the one or more correlations between therespective items of interest (e.g., describing how a change with regardto a first item of interest can affect or change a second item ofinterest) and/or one or more recommendations or instructions that can beconsidered or acted upon by the user to facilitate improving operationsassociated with the industrial automation system.

At 2212, the customized information display can be presented (e.g., tothe user via the user interface of the user's communication device orother user interface available to the user). The analytics component canpresent (e.g., communicate) the customized information display to thecommunication device of the user, wherein the customized informationdisplay can be displayed on a display screen of the communicationdevice.

Embodiments, systems, and components described herein, as well asindustrial automation or control systems and industrial automationenvironments in which various aspects set forth in the subjectspecification can be carried out, can include computer or networkcomponents such as servers, clients, programmable logic controllers(PLCs), automation controllers, communications modules, mobilecomputers, wireless components, control components and so forth whichare capable of interacting across a network. Computers and serversinclude one or more processors—electronic integrated circuits thatperform logic operations employing electric signals—configured toexecute instructions stored in media such as random access memory (RAM),read only memory (ROM), a hard drives, as well as removable memorydevices, which can include memory sticks, memory cards, flash drives,external hard drives, and so on.

Similarly, the term PLC or automation controller as used herein caninclude functionality that can be shared across multiple components,systems, and/or networks. As an example, one or more PLCs or automationcontrollers can communicate and cooperate with various network devicesacross the network. This can include substantially any type of control,communications module, computer, Input/Output (I/O) device, sensor,actuator, and human machine interface (HMI) that communicate via thenetwork, which includes control, automation, and/or public networks. ThePLC or automation controller can also communicate to and control variousother devices such as I/O modules including analog, digital,programmed/intelligent I/O modules, other programmable controllers,communications modules, sensors, actuators, output devices, and thelike.

The network can include public networks such as the internet, intranets,and automation networks such as control and information protocol (CIP)networks including DeviceNet, ControlNet, and Ethernet/IP. Othernetworks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus,Profibus, CAN, wireless networks, serial protocols, and so forth. Inaddition, the network devices can include various possibilities(hardware and/or software components). These include components such asswitches with virtual local area network (VLAN) capability, LANs, WANs,proxies, gateways, routers, firewalls, virtual private network (VPN)devices, servers, clients, computers, configuration tools, monitoringtools, and/or other devices.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 23 and 24 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattercan be implemented.

With reference to FIG. 23, an example environment 2300 for implementingvarious aspects of the aforementioned subject matter includes a computer2312. The computer 2312 includes a processing unit 2314, a system memory2316, and a system bus 2318. The system bus 2318 couples systemcomponents including, but not limited to, the system memory 2316 to theprocessing unit 2314. The processing unit 2314 can be any of variousavailable processors. Multi-core microprocessors and othermultiprocessor architectures also can be employed as the processing unit2314.

The system bus 2318 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 8-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 2316 includes volatile memory 2320 and nonvolatilememory 2322. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer2312, such as during start-up, is stored in nonvolatile memory 2322. Byway of illustration, and not limitation, nonvolatile memory 2322 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable PROM (EEPROM), or flashmemory. Volatile memory 2320 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM).

Computer 2312 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 23 illustrates, forexample a disk storage 2324. Disk storage 2324 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 2324 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 2324 to the system bus 2318, a removableor non-removable interface is typically used such as interface 2326.

It is to be appreciated that FIG. 23 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 2300. Such software includes an operatingsystem 2328. Operating system 2328, which can be stored on disk storage2324, acts to control and allocate resources of the computer 2312.System applications 2330 take advantage of the management of resourcesby operating system 2328 through program modules 2332 and program data2334 stored either in system memory 2316 or on disk storage 2324. It isto be appreciated that one or more embodiments of the subject disclosurecan be implemented with various operating systems or combinations ofoperating systems.

A user enters commands or information into the computer 2312 throughinput device(s) 2336. Input devices 2336 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 2314through the system bus 2318 via interface port(s) 2338. Interfaceport(s) 2338 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 2340 usesome of the same type of ports as input device(s) 2336. Thus, forexample, a USB port may be used to provide input to computer 2312, andto output information from computer 2312 to an output device 2340.Output adapters 2342 are provided to illustrate that there are someoutput devices 2340 like monitors, speakers, and printers, among otheroutput devices 2340, which require special adapters. The output adapters2342 include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 2340and the system bus 2318. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 2344.

Computer 2312 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)2344. The remote computer(s) 2344 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer2312. For purposes of brevity, only a memory storage device 2346 isillustrated with remote computer(s) 2344. Remote computer(s) 2344 islogically connected to computer 2312 through a network interface 2348and then physically connected via communication connection 2350. Networkinterface 2348 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 2350 refers to the hardware/softwareemployed to connect the network interface 2348 to the system bus 2318.While communication connection 2350 is shown for illustrative clarityinside computer 2312, it can also be external to computer 2312. Thehardware/software necessary for connection to the network interface 2348includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 24 is a schematic block diagram of a sample computing and/ornetworking environment 2400 with which the disclosed subject matter caninteract. The computing and/or networking environment 2400 can includeone or more clients 2402. The client(s) 2402 can be hardware and/orsoftware (e.g., threads, processes, computing devices). The computingand/or networking environment 2400 also can include one or more servers2404. The server(s) 2404 can also be hardware and/or software (e.g.,threads, processes, computing devices). The servers 2404 can housethreads to perform transformations by employing one or more embodimentsas described herein, for example. One possible communication between aclient 2402 and servers 2404 can be in the form of a data packet adaptedto be transmitted between two or more computer processes. The computingand/or networking environment 2400 can include a communication framework2406 that can be employed to facilitate communications between theclient(s) 2402 and the server(s) 2404. The client(s) 2402 are operablyconnected to one or more client data stores 2408 that can be employed tostore information local to the client(s) 2402. Similarly, the server(s)2404 are operably connected to one or more server data stores 2410 thatcan be employed to store information local to the servers 2404.

What has been described above includes examples of the disclosed subjectmatter. It is, of course, not possible to describe every conceivablecombination of components or methods for purposes of describing thedisclosed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of thedisclosed subject matter are possible. Accordingly, the disclosedsubject matter is intended to embrace all such alterations,modifications, and variations that fall within the spirit and scope ofthe appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the disclosed subjectmatter. In this regard, it will also be recognized that the disclosedsubject matter includes a system as well as a computer-readable mediumhaving computer-executable instructions for performing the acts and/orevents of the various methods of the disclosed subject matter.

In addition, while a particular feature of the disclosed subject mattermay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes,” and “including” and variants thereof are used ineither the detailed description or the claims, these terms are intendedto be inclusive in a manner similar to the term “comprising.”

It is to be appreciated and understood that components (e.g., modelercomponent, model management component, virtualization component,collection component, communication device, information providercomponent, processor component, data store, etc.), as described withregard to a particular system or method, can include the same or similarfunctionality as respective components (e.g., respectively namedcomponents or similarly named components) as described with regard toother systems or methods disclosed herein.

In this application, the word “exemplary” is used to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

What is claimed is:
 1. A system, comprising: a memory that storescomputer-executable components; a processor, operatively coupled to thememory, that executes computer-executable components, thecomputer-executable components comprising: a collection componentcollects a set of industrial data from a set of devices of an industrialautomation system and store the set of industrial data in a data store;and an analytics component that performs analytics on the set ofindustrial data to determine a correlation between respective items ofinterest associated with the industrial automation system to facilitateimproving operations or performance associated with the industrialautomation system.
 2. The system of claim 1, wherein the analyticscomponent determines a change relating to the industrial automationsystem that is determined to result in improving the operations or theperformance associated with the industrial automation system based atleast in part on the correlation between the respective items ofinterest associated with the industrial automation system.
 3. The systemof claim 2, wherein the analytics component generates a recommendationmessage comprising a recommendation to implement the change relating tothe industrial automation system to facilitate improving the operationsor the performance associated with the industrial automation system, andcommunicates the recommendation message to a communication deviceassociated with the user to facilitate implementation of the change. 4.The system of claim 2, wherein the analytics component generates aninstruction message comprising a set of instructions to implement thechange relating to the industrial automation system to facilitateimproving the operations or the performance associated with theindustrial automation system, and communicates the instruction messageto an industrial device of the set of devices to facilitateimplementation of the change.
 5. The system of claim 4, wherein thechange relates to at least one of a modification of a parameterassociated with the industrial device, or a modification of aconfiguration associated with the industrial device.
 6. The system ofclaim 2, wherein the respective items of interest associated with theindustrial automation system comprise a first item of interest and asecond item of interest.
 7. The system of claim 6, wherein the firstitem of interest relates to at least one of an industrial device of theset of devices, a network-related device of the set of devices, anindustrial process of the industrial automation system, a productionoutput for a product produced by the industrial automation system, aproduction goal associated with the product, a product cost associatedwith producing the product, a material that is used for producing theproduct and stored in an industrial facility associated with theindustrial automation system, or an interaction of an employee with theindustrial automation system, a defined period of time associated withthe industrial automation system
 8. The system of claim 7, wherein thesecond item of interest relates to at least one of another industrialdevice of the set of devices, another network-related device of the setof devices, another industrial process of the industrial automationsystem, another production goal associated with the product, anothermaterial that is used for producing the product and stored in theindustrial facility or at a location other than the industrial facility,another interaction of another employee with the industrial automationsystem, another defined period of time associated with the industrialautomation system, a weather condition, a transportation cost relatingto the product, an energy cost associated with the industrial automationsystem, a governmental policy, or a consumer demand for the product. 9.The system of claim 1, wherein the computer-executable componentsfurther comprise a visualization component that generates a visualizedinformation display comprising information relating to at least one of aportion of the industrial data or a result of the analytics performed onthe set of industrial data.
 10. The system of claim 9, wherein thevisualized information display is a two-dimensional visualizedinformation display or a three-dimensional visualized informationdisplay.
 11. The system of claim 9, wherein the analytics componentreceives input information from a communication device associated with auser, wherein the input information indicates a selection of a firstitem of interest associated with the industrial automation system and asecond item of interest associated with the industrial automation systemby the user.
 12. The system of claim 11, wherein the correlation betweenthe respective items of interest associated with the industrialautomation system is a correlation between the first item of interestand the second item of interest, and wherein, in response to theselection of the first item of interest and the second item of interest,the analytics component determines the correlation between the firstitem of interest and the second item of interest based at least in parton a result of the analytics performed on the set of industrial data.13. The system of claim 12, wherein the visualization component modifiesthe visualized information display to generate a modified visualizedinformation display comprising a subset of visualized information thatpresents a visual representation relating to the correlation between thefirst item of interest and the second item of interest, and presents themodified visualized information display to the communication deviceassociated with the user.
 14. The system of claim 9, wherein theanalytics component analyzes a set of correlations between therespective items of interest associated with the industrial automationsystem, determines respective priority levels of respective correlationsof the set of correlations, and ranks the respective correlations basedat least in part on the respective priority levels of the respectivecorrelations, in accordance with a defined analytics criterion.
 15. Thesystem of claim 14, wherein the visualization component generates thevisualized information display comprising visualized informationrelating to the respective correlations, wherein the respectivecorrelations are presented in the visualized information display inorder of the respective priority levels of the respective correlations,and wherein the visualization component presents the visualizedinformation display to a communication device associated with a user.16. The system of claim 1, wherein the analytics component and thecollection component are associated with a set of industrial automationsystems comprising the industrial automation system and a secondindustrial automation system, wherein the collection component collectsa second set of industrial data from a second set of devices of thesecond industrial automation system and store the second set ofindustrial data in the data store, and wherein the analytics componentperforms analytics on the set of industrial data and the second set ofindustrial data to determine a second correlation between a first itemof interest associated with the industrial automation system and asecond item of interest associated with the second industrial automationsystem to facilitate improving operations or performance associated withthe set of industrial automation systems.
 17. The system of claim 1,wherein the analytics component determines a performance baseline for avariable relating to the industrial automation system based at least inpart on a result of the analytics performed on the set of industrialdata, wherein the performance baseline relates to a defined productiongoal associated with the industrial automation system and indicates adefined acceptable performance level for the variable.
 18. The system ofclaim 17, wherein the analytics component determines a deviation fromthe performance baseline based at least in part on second analyticsperformed on a subset of the set of industrial data, wherein at least aportion of the subset of the industrial data relates to the variable.19. The system of claim 1, wherein the analytics component receives aset of video streams from a set of capture components associated withthe industrial automation system, wherein respective capture componentsof the set of capture components capture video of respective portions ofthe industrial automation system.
 20. The system of claim 19, whereinthe analytics component analyzes the set of video streams to facilitatedetermining the correlation between the respective items of interestassociated with the industrial automation system.
 21. The system ofclaim 1, wherein at least one of the collection component, the analyticscomponent, or the data store are part of a cloud platform, and whereinthe computer-executable components further comprise an interfacecomponent configured to interface at least one of the collectioncomponent, the analytics component, or the data store of the cloudplatform with the industrial automation system via a cloud gatewaycomponent of the industrial automation system to facilitatecommunication of the set of industrial data from the industrialautomation system to at least one of the collection component, theanalytics component, or the data store.
 22. The system of claim 21,wherein an industrial device of the set of devices is integrated with orassociated with the cloud gateway component to facilitate communicationof a subset of the set of industrial data relating to the industrialdevice from the industrial device to at least one of the collectioncomponent, the analytics component, or the data store.
 23. The system ofclaim 1, wherein the set of industrial data comprises at least one ofdata relating to an industrial device of the set of devices, datarelating to an industrial process associated with the set of devices,data relating to an industrial asset, data relating to a network-relateddevice of the set of devices that facilitates data communicationsassociated with the industrial automation system, data relating to atleast one interrelationship between the at least one device and at leastone other device of the set of devices, data relating to an operatingsystem associated with the industrial automation system, data relatingto software associated with the industrial automation system, or datarelating to firmware associated with the industrial automation system.24. A method, comprising: receiving, by a system comprising a processor,a set of industrial data from a set of devices of an industrialautomation system for storage in a data store associated with theindustrial automation system; and performing, by the system, analyticson the set of industrial data to at least determine a correlationbetween respective items of interest associated with the industrialautomation system to facilitate enhancing performance with respect tothe industrial automation system.
 25. The method of claim 24, furthercomprising: based at least in part on the correlation between therespective items of interest associated with the industrial automationsystem, determining, by the system, a modification relating to theindustrial automation system that is determined to result in enhancingthe performance with respect to the industrial automation system. 26.The method of claim 25, further comprising: generating, by the system, arecommendation to implement the modification relating to the industrialautomation system to facilitate enhancing the performance with respectto the industrial automation system; and transmitting, by the system,the recommendation to a communication device associated with the user tofacilitate implementing the modification.
 27. The method of claim 25,further comprising: generating, by the system, a set of instructionsthat instruct an industrial device of the set of devices to implementthe modification relating to the industrial automation system tofacilitate enhancing the performance with respect to the industrialautomation system; and transmitting the set of instructions to theindustrial device to facilitate implementing the modification.
 28. Themethod of claim 24, further comprising: generating, by the system, avisualized information display comprising information that visuallyrepresents at least one of a portion of the industrial data or a resultof the analytics performed on the set of industrial data.
 29. The methodof claim 28, wherein the visualized information display is atwo-dimensional visualized information display or a three-dimensionalvisualized information display.
 30. The method of claim 28, furthercomprising: obtaining, by the system, selection information from acommunication device associated with a user, wherein the selectioninformation indicates a selection of a first item of interest associatedwith the industrial automation system and a second item of interestassociated with the industrial automation system by the user; and inresponse to the selecting of the first item of interest and the seconditem of interest, determining, by the system, a correlation between thefirst item of interest and the second item of interest based at least inpart on a result of the analytics performed on the set of industrialdata.
 31. The method of claim 30, further comprising: modifying, by thesystem, the visualized information display to generate an augmentedvisualized information display comprising visualized information thatpresents a visual representation relating to the correlation between thefirst item of interest and the second item of interest; andtransmitting, by the system, the augmented visualized informationdisplay to the communication device associated with the user.
 32. Themethod of claim 28, further comprising: analyzing, by the system, a setof correlations between the respective items of interest associated withthe industrial automation system; determining, by the system, respectivepriority levels of respective correlations of the set of correlations;and arranging, by the system, the respective correlations in a priorityranking order based at least in part on the respective priority levelsof the respective correlations, in accordance with a defined analyticscriterion.
 33. The method of claim 32, further comprising: generating,by the system, the visualized information display comprising visualizedinformation relating to the respective correlations that presents therespective correlations in accordance with the priority ranking order;and transmitting, by the system, the visualized information display to acommunication device associated with a user.
 34. The method of claim 24,further comprising: identifying, by the system, a performance baselinefor a variable relating to the industrial automation system based atleast in part on a result of the analytics performed on the set ofindustrial data, wherein the performance baseline relates to a definedproduction goal associated with the industrial automation system andspecifies a defined threshold acceptable performance level for thevariable.
 35. The method of claim 34, further comprising: determining,by the system, a deviation from the performance baseline based at leastin part on a result of second analytics performed on a subset of the setof industrial data, wherein at least a portion of the subset of theindustrial data relates to the variable.
 36. The method of claim 24,further comprising: receiving, by the system, a set of video streamsfrom a set of capture devices associated with the industrial automationsystem, wherein respective capture devices of the set of capture devicescapture video of respective portions of the industrial automationsystem; and analyzing, by the system, the set of video streams, whereinthe determining the correlation further comprises determining thecorrelation between the respective items of interest associated with theindustrial automation system based at least in part on a result of theanalyzing of the set of video streams.
 37. A non-transitorycomputer-readable storage medium storing computer-executableinstructions that, in response to execution, cause a system comprising aprocessor to perform operations, comprising: collecting a set ofindustrial data from a set of devices of an industrial automation systemfor storage in a data store associated with the industrial automationsystem; and performing analytics on the set of industrial data to atleast determine a correlation between a first item of interestassociated with the industrial automation system and a second item ofinterest associated with the industrial automation system to facilitateimproving performance associated with the industrial automation system.38. The non-transitory computer-readable storage medium of claim 37,wherein the operations further comprise: based at least in part on thecorrelation between the respective items of interest associated with theindustrial automation system, determining a modification relating to theindustrial automation system that is determined to result in improvingthe performance associated with the industrial automation system;generating a recommendation or an instruction that recommends orinstructs implementing the modification relating to the industrialautomation system to facilitate the improving of the performanceassociated with the industrial automation system; and transmitting therecommendation or the instruction to a communication device associatedwith the user or an industrial device of the set of devices tofacilitate implementing the modification.